9  All Code together

Code
library(tidyverse)
library(haven)
library(formatR)
library(lubridate)
library(smooth)
library(forecast)
library(scales)
library(kableExtra)
library(ggplot2)
library(readxl)
library(tidyverse)
library(data.table)
library(quantmod)
library(geofacet)
library(janitor)


knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE)

exp_temp <- read_csv("exp_temp.csv")
rev_temp <- read_csv("rev_temp.csv")

9.1 Pension Discusion

Code
# check what is being included in pensions

# funds related to pension contributions
pension_funds <- c("0472", "0473", "0477", "0479", "0481", "0755", "0786", "0787", "0788", "0789", "0799")

pension_check <- exp_temp %>% 
  mutate(pension = case_when( 
 # object == "4430" & fund == "0825" ~ "Object 4430 - Pension Buyout/Benefits Paid Early",
    (object=="4430") ~ "Object 4430 - Benefits Paid to Employees; EXCLUDED", # pensions, annuities, benefits
    (object=="4431") ~ "Object 4431 - State Contributions; INCLUDED", # 4431 = state payments into pension fund
        (obj_seq_type > "11590000" & obj_seq_type < "11660000")  ~ "Object 1160-1165 Employer Contributions to Pension Fund; EXCLUDED",
    # objects 1159 to 1166 are all considered Retirement by Comptroller 
    
            TRUE ~ "0")) %>%  # All other observations coded as 0 for non-pension items
  
  # recodes specific instances of code anomalies from past years:
  mutate(pension = case_when(
    (object=="1298" & fund %in% pension_funds ) ~ "Object 1298 - Purchase of Investments; DROPPED", 

    
      # pension stabilization fund in 2022 
 # object == "1900" & fund == "0319" ~ "Fund 0319-Pension Stabilization", 
    object == "1900" & fund %in% pension_funds ~ "Fund 0319 - Pension Stabilization", 

  
  object == "4900" & fund %in% pension_funds ~ "Object 4900 - Awards/Grants; Weird 2010-2011 values",
  
    TRUE ~ as.character(pension)) ) %>% 
  filter(pension != "0" )

pension_check %>% group_by(fy, pension) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, color = pension)) + 
  geom_line() + 

  labs (title = "Pension Fund Payments In and Retirement Benefits Out", 
  caption = "Object 4430 is retirement benefits paid to employees. 
  Object 4431 includes state payments INTO pension Fund.
  Object 1998 is excluded except for years 2010 and 2011 due to POBs.")+
    theme(legend.position = "bottom")+
  guides(color = guide_legend(nrow=3))

Code
pension_check %>% group_by(fy, object) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, color = object)) + 
  geom_line() + 
  labs (title = "Expenditures by Object")

Code
pension_check %>% group_by(fy, type) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, color = type)) + 
  geom_line() + 
  labs (title = "Expenditures by Type", caption = "Not confident with what Type represents. 
        $10 billion POB issued in 2003-2004 and again in 2010-2011.")

Code
pension_check3 <- exp_temp %>% 
  mutate(pension = case_when( 
       (object=="4430"  ) ~ 1, # 4430 = pension benefits paid to retired employees
            TRUE ~ 0)) %>% 
  filter(pension > 0 )

pension_check3 %>% group_by(fy) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure)) + 
  geom_line() + 
  labs (title = "Pension Benefits Paid to Employees")

Code
## taking care of Pension Obligation Bond proceeds

pension_picture <- exp_temp %>% 
  mutate(pension = case_when( 
  #object == "4430" & fund == "0825" ~ "Pension Buyout/Benefits Paid Early; INCLUDED",
    (object=="4430") ~ "Benefits Paid to Employees", # pensions, annuities, benefits
    (object=="4431") ~ "State Pension Contributions", # 4431 = state payments into pension fund
        (obj_seq_type > "11590000" & obj_seq_type < "11660000")  ~ "IOC Retirement Expense Objectw",
    # objects 1159 to 1166 are all considered Retirement by Comptroller 
    
            TRUE ~ "0")) %>%  # All other observations coded as 0 for non-pension items
  
  # recodes specific instances of code anomalies from past years:
#  mutate(pension = case_when( (object=="1298" & fund %in% pension_funds ) ~ "Purchase of Investments", 

    
      # pension stabilization fund in 2022 
 # object == "1900" & fund == "0319" ~ "Fund 0319-Pension Stabilization", 
   # object == "1900" & fund %in% pension_funds ~ "Pension Stabilization Fund", 
  
    #TRUE ~ as.character(pension)) ) %>% 
  filter(pension != "0" )

pension_picture %>% group_by(fy, pension) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, color = pension)) + 
  geom_line() + 

  labs (title = "Pension Fund Payments In and Retirement Benefits Out", 
  caption = "All pension expenditure types are included in IOC Expenditure data")+
    theme(legend.position = "bottom", legend.title = element_blank())#+ guides(color = guide_legend(nrow=2))

9.1.1 Pension Contributions - Revenue Data

Code
# rev_type = 51 is for retirement/pension contributions from both employers and employees.

# current year employee revenue source = 0573, contributions by employee == 572 (stops at 2011)
retirement_contributions <- rev_temp %>% 
  filter(rev_type == "51") %>% group_by(fy) %>% summarize(contributions = sum(receipts))

employer_contributions <- rev_temp %>% 
  filter(rev_type == "51" & source == "0577") %>% group_by(fy) %>% summarize(contributions = sum(receipts))

employee_contributions <- rev_temp %>% 
  filter(rev_type == "51" & (source == "0572" | source == "0573") ) %>% 
  group_by(fy) %>% summarize(contributions = sum(receipts))

benefits_paid <- pension_check %>% filter(object == "4430") %>%
  group_by(fy) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE))

state_contrib <- pension_check %>% filter(object == "4431") %>%
  group_by(fy) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE))

rev_temp %>% 
  filter(rev_type == "51") %>% # all retirement contributions
  group_by(fy, source) %>% 
  summarise(sum = sum(receipts, na.rm = TRUE)) %>%
  ggplot() +
  geom_line(aes(x=fy, y = sum, color=source)) + labs(title="All Retirement Contributions, ALL rev_source == 51", 
       caption = "Source 0573, 0572 is for employee contributions. 0577 is Contributions by employer.")

Code
# # contributions and benefits paid comparison
# ggplot()+
#   geom_line(data=employee_contributions, aes(x=fy, y=contributions), color=" light green") +
#     geom_line(data=employer_contributions, aes(x=fy, y=contributions), color="orange") +
# 
#   geom_line(data= state_contrib, aes(x=fy, y = expenditure), color = "red")+ 
# 
#   geom_line(data= benefits_paid, aes(x=fy, y = expenditure), color = "dark blue")+ 
#   labs(title="Pension fund inflows and outflows", 
#        caption = "Blue: object = 4430 for benefits paid out of funds, 
#       red:  object = 4431 for state contributions into pension fund, 
#     neon  green: employee contributions into the fund,
#      orange: employer contributions into the fund.", y = "Dollars")

pension_picture %>% group_by(fy, pension) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, color = pension)) + 
  geom_line() + 
  geom_line(data=employee_contributions, aes(x=fy, y=contributions), color="green") +
    geom_line(data=employer_contributions, aes(x=fy, y=contributions), color="orange") +
  labs (title = "Pension Fund Payments In and Retirement Benefits Out", 
  caption = "Neon green - employee contributions INTO the fund. 
  Orange - employer contributions INTO the fund.")+
    theme(legend.position = "bottom", legend.title = element_blank())

9.2 Debt Service Discussion

Code
tollway <- exp_temp %>% filter(fund == "0455") #all tollway expenditures
capitalproject_debtservice <- exp_temp %>%filter(object == "8800") # ALL Capital projects debt service

# look at Illinois tollway bond proceeds and debt service: 
# rev_temp %>% filter(fund == "0455") # examine items in fund 0455
#exp_temp %>% filter(fund == "0455") %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy)
tollway
Code
#rev_temp %>% filter(fund == "0455") %>% group_by(fy) %>% summarize(sum = sum(receipts)) %>% arrange(-fy)


tollway_exp <- exp_temp %>% filter(fund == "0455") %>% group_by(fy) %>% summarize(expenditure = sum(expenditure))
                                                                                                    #tollway_exp %>% ggplot() + geom_line(aes(x=fy, y=expenditure)) + labs(title = "Fund 0455 from Expenditure: All Tollway Expenditures", caption = "Data from IOC Expenditure Files. Fund 0455 is the IL State Tollway")


# all tollway revenues, not just bond proceeds
alltollway<-rev_temp %>% filter(fund == "0455" & source != "0571") %>% group_by(fy) %>% summarize(sum = sum(receipts, na.rm = TRUE))


# tollway bond proceeds
tollway_bondproc <- rev_temp %>% filter(fund == "0455" & source == "0571" ) %>% group_by(fy) %>% summarize(sum = sum(receipts, na.rm = TRUE))

#alltollway %>%  ggplot() + geom_line(aes(x=fy, y=sum)) + labs(title = "Fund 0455 - All Tollway Revenue", caption = "Data from IOC Revenue Files. Fund 0455 is the IL State Tollway Revenue") 

#tollway_bondproc %>% ggplot() + geom_line(aes(x=fy, y=sum)) + labs(title = "Fund 0455 - Tollway Revenue: Tollway Bond Proceeds", caption = "Data from IOC Revenue Files. Fund 0455 is the IL State Tollway Revenue")
  
#ggplot() + geom_line(data=tollway_bondproc, aes(x=fy, y=sum)) + labs(title = "Fund 0455 - Tollway Revenue: Tollway Bond Proceeds", caption = "Data from IOC Revenue Files. Fund 0455 is the IL State Tollway Revenue")

#tollwaydebt %>% ggplot() + geom_line(aes(x=fy, y=sum)) + labs(title = "Tollway Debt Service", caption = "Debt service includes principal and interest for the Illinois Tollway. Object = 8800 and fund = 0455")


#tollway debt principal and interest
tollwaydebt <- exp_temp %>%filter(object == "8800" & fund == "0455") %>% group_by(fy) %>% summarize(sum=sum(expenditure)) 

# Tollway agency expenditures = SAME as filtering by fund == 0455
#tollway<-exp_temp %>% filter(agency == "557")
#exp_temp %>% filter(agency == "557") %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy)

# contributions and benefits paid comparison
ggplot()+
  geom_line(data=tollway_bondproc, aes(x=fy, y=sum, color='Bond Proceeds')) +
  geom_line(data= tollwaydebt, aes(x=fy, y = sum, color = 'Debt Service'))+ 
  geom_line(data= tollway_exp, aes(x=fy, y = expenditure, color = 'Tollway Expenditures'))+ 
  geom_line(data= alltollway, aes(x=fy, y = sum, color = "Tollway Revenue"))+ 
  scale_color_manual(values = c(
    'Bond Proceeds' = 'darkblue',
    'Debt Service' = 'red',
    'Tollway Expenditures' = 'orange',
    'Tollway Revenue' = 'light green')) +
  labs(title="Tollway bond procreeds, debt service, revenue, and expenditures.", 
       caption = "Tollway revenue + bond proceeds should be roughly equal to tollway expenditures + debt service.", 
       y = "Dollars")

9.2.0.1 State Principal and Interest

Filtering for interest on short term borrowing and GO bonds (88130008, 88130000, and 88130108) and GO bond principal amounts (88110008).

  • object == 8813 is for all debt service interest but obj_seq_type is used to specify short term borrowing versus regular debt service.

  • an Interest to Principal ratio is also calculated in the table below.

Looking only at general obligation principal payments and interest payments:

Code
# GO bond principal and GO bond interest
GObond_debt <- exp_temp %>% 
  filter(obj_seq_type == "88110008" |obj_seq_type == "88130000" | obj_seq_type == "88130008") %>% 
  group_by(fy, obj_seq_type) %>% 
  summarize(sum = sum(expenditure, na.rm=TRUE)) %>% 
  pivot_wider(names_from = obj_seq_type, values_from = sum) %>% 
  mutate(principal = `88110008`,
         interest = sum(`88130008`+`88130000`, na.rm = TRUE),
         ratio = (as.numeric(interest)/as.numeric(principal)))

GObond_debt %>% select(principal, interest, ratio) %>%
  mutate(across(principal:interest, ~format(., big.mark= ",", scientific = F)))
Code
# GObond_debt %>% ggplot() + 
#   geom_line(aes(x=fy, y=principal, color = "Principal"))+ 
#   geom_line(aes(x=fy, y=interest, color = "Interest")) + 
#   labs(title = "General Obligation principal and interest payments")

GObond_debt %>% ggplot() +   
  geom_col(aes(x=fy, y=interest/1000000, fill = "Interest")) + 
  geom_col(aes(x=fy, y=principal/1000000, fill = "Principal"))+ 
  labs(title = "Debt Service", subtitle = "General Obligation Principal and Interest Payments")

Looking only at short term borrowing principal and interest payments:

Code
# short term borrowing, first observation is in 2004?
short_debt <- exp_temp %>% 
  filter(obj_seq_type == 88110108 |obj_seq_type == 88130108) %>% 
  group_by(fy, obj_seq_type) %>% 
  summarize(sum = sum(expenditure, na.rm=TRUE)) %>% 
  pivot_wider(names_from = obj_seq_type, values_from = sum) %>% 
  mutate(principal = `88110108`,
         interest = `88130108`,
         ratio = (as.numeric(interest)/as.numeric(principal)))

short_debt %>% select(principal, interest, ratio) %>%
  mutate(across(principal:interest, ~format(., big.mark= ",",  scientific = F)))
Code
short_debt %>% ggplot() + 
  geom_col(aes(x=fy, y=principal/1000000, fill = "Principal"))+ 
  geom_col(aes(x=fy, y=interest/1000000, fill = "Interest")) + 
  labs(title = "Debt Service", subtitle = "Short Term Borrowing: Principal and Interest Payments")

When including short term borrowing and normal debt service, the debt ratio seems more normal and the total interest and principal payments over the years are smoothed out.

Principal and interest amounts calculated exclude the Illinois Tollway debt service and debt for capital projects. Capital projects debt service is examined below:

Code
capitalprojects <- exp_temp %>%filter(object == "8800")

all_debt <- exp_temp %>% 
  filter(fund != "0455" & (object == "8811" |object == "8813" | object == "8800") )%>% 
  group_by(fy, object) %>% 
  summarize(sum = sum(expenditure, na.rm=TRUE)) %>% 
  pivot_wider(names_from = object, values_from = sum) %>% 
  mutate(principal = `8811`,
         interest = `8813`,
         CapitalProjects = `8800`,
         ratio = (as.numeric(interest)/as.numeric(principal)))

all_debt %>% select(principal, interest, CapitalProjects, ratio) %>%
  mutate(across(principal:CapitalProjects, ~format(., big.mark= ",", scientific = F)))
Code
all_debt %>% ggplot() + 
  geom_line(aes(x=fy, y=principal/1000000, color = "Principal"))+ 
  geom_line(aes(x=fy, y=interest/1000000, color = "Interest"))+
  geom_line(aes(x=fy, y = CapitalProjects / 1000000, color = "Capital Projects"))+
  labs(y = "Debt ($Millions)",
       title = "Principal and Interest payments", subtitle = "Principal and interest from short term borrowing and GO Bonds debt service", caption = "Capital projects does not include Illinois tollway debt service.
       Capital projects include interest and principal values as one value and cannot be sepearated.")

Code
all_debt %>% ggplot() + 
  geom_line(aes(x=fy, y=principal/1000000, color = "Principal"))+ 
  geom_line(aes(x=fy, y=interest/1000000, color = "Interest"))+
  geom_line(aes(x=fy, y = CapitalProjects / 1000000, color = "Capital Projects Debt Service"))+
  geom_line(data = tollwaydebt, aes( x=fy, y=sum/1000000, color = "Tollway Debt Service"))+
  labs(y = "Debt ($Millions)", title = "Short term borrowing and GO Bonds",
       subtitle = "Principal and Interest payments", caption = "Capital projects does not include Illinois tollway debt service.") 

Code
all_debt %>% ggplot() + 
  geom_line(aes(x=fy, y=(principal+interest)/1000000, color = "Principal & Interest"))+ 
  #geom_line(aes(x=fy, y=interest/1000000, color = "Interest"))+
  geom_line(aes(x=fy, y = CapitalProjects / 1000000, color = "Capital Projects Debt Service"))+
  geom_line(data = tollwaydebt, aes( x=fy, y=sum/1000000, color = "Tollway Debt Service"))+
  labs(y = "Debt ($Millions)", title = "Illinois Debt Service Expenditures: Short term borrowing and GO Bonds",
       subtitle = "Principal and Interest payments", caption = "Capital projects does not include Illinois tollway debt service.") 

Capital projects include the IL Civic Center and Build Illinois Bonds. Tollway principal and interest has been dropped from the State’s Debt Service expenditure but is counted in the Illinois Tollway Expenditure cost.

9.3 State Employee Healthcare Discussion

Code
health_ins_reserve <- exp_temp %>% filter(fund == "0907") %>%  group_by(fy) %>% 
    summarize(fund_0907 = sum(expenditure)) 

health_ins_reserve %>% 
  ggplot(aes(x=fy, y=fund_0907)) + geom_line() + labs(title="Health Insurance Reserve", subtitle = "Sum of expenditures from fund 907")

Code
# object 1180 is inconsistently coded over time form the IOC 
# object 1180 should be employer contributions to healthcare group insurance
employer_contributions <- exp_temp %>% filter(object == "1180") %>% group_by(fy) %>% summarize(object1180 = sum(expenditure)) 

employer_contributions%>% 
  ggplot(aes(x=fy, y=object1180)) + geom_line() + labs(title="Employer Contributions to Healthcare Group Insurance, IOC Object 1180")

Code
employer_contributions2 <- exp_temp %>% filter(object == "1180" & fund=="0001") %>% group_by(fy) %>% summarize(object1180 = sum(expenditure)) 

employer_contributions2 %>% 
  ggplot(aes(x=fy, y=object1180)) + geom_line() + labs(title="Employer Contributions to Healthcare Group Insurance", subtitle = "IOC Object 1180 from Fund 001")

Code
# examine combined group insurance totals per year
group_ins2 <- exp_temp %>% 
  mutate(eehc = ifelse(
    # group insurance contributions for 1998-2005 and 2013-present
    # CMS took over health insurance in 2013
   fund == "0001" & (object == "1180" | object =="1900") & agency == "416" & appr_org=="20", 1, 0) )%>% 
  mutate(eehc = ifelse(
    # group insurance contributions for 2006-2012
    # health insurance was in healthcare and family services, agency 478 for a few years
    fund == "0001" & object == "1180" & agency == "478" & appr_org=="80", 1, eehc) )%>%
  filter(eehc == 1) %>% 
    group_by(fy) %>% 
    summarize(dropped_group_premiums = sum(expenditure, na.rm=TRUE))


group_ins2 %>% ggplot(aes(x=fy, y=dropped_group_premiums)) + geom_line() + labs(title="Employer Healthcare Group Insurance Contributions", subtitle= " - Dropped from analysis to avoid double counting healthcare expenditures", caption = "Objects 1180 and 1900 from fund 0001. See code for additional coding details.")

9.3.1 Health Insurance Premiums - Revenue Side

Code
health_insurance_fund_rev<- rev_temp %>% 
  filter(fund=="0907") %>% 
    group_by(fy) %>%
  summarize(health_ins_rev = sum(receipts)) 

health_insurance_fund_rev %>% 
  ggplot(aes(x=fy, y = health_ins_rev)) + 
  geom_line() + labs( title = "Health insurance fund - All revenue, Fund 0907")

Code
#collect optional insurance premiums to fund 0907 for use in eehc expenditure  
employee_health_premiums <- rev_temp %>% 
  mutate(employee_premiums = ifelse(
    fund=="0907" & (source=="0120"| source=="0121"| (source>"0345" & source<"0357")|(source>"2199" & source<"2209")), 1, 0)) %>%
  filter(employee_premiums == 1)

# optional insurance premiums = employee insurance premiums
emp_premium <- employee_health_premiums %>%
  group_by(fy) %>%
  summarize(employee_premiums_sum = sum(receipts))
  
emp_premium %>% ggplot(aes(x=fy, y = employee_premiums_sum)) + 
  geom_line() + labs( title = "Employee health insurance premiums")

Code
# contributions and benefits paid comparison
ggplot()+
  #  geom_line(data=group_ins, aes(x=fy, y=object1180, color='Group Insurance1')) +
      geom_line(data=health_insurance_fund_rev, aes(x=fy, y=health_ins_rev, color='Health Insurance Fund - All Revenue')) +
 geom_line(data = emp_premium, aes(x=fy, y = employee_premiums_sum, color = 'Revenue from Employee Premiums')) +
    geom_line(data=health_ins_reserve, aes(x=fy, y=fund_0907, color='Cost of Provision')) +
    geom_line(data=employer_contributions, aes(x=fy, y=object1180, color='Group Insurance-Object1180')) +
 #   geom_line(data=employer_contributions2, aes(x=fy, y=object1180, color='Employer Contributions-General Fund')) +

  geom_line(data=group_ins2, aes(x=fy, y=dropped_group_premiums, color='Group Insurance - 1180 & 1900')) +
  #geom_line(data= healthcare_costs, aes(x=fy, y = cost_of_provision, color = 'Healthcare Costs'))+ 
  scale_color_manual(values = c(
    'Cost of Provision' = 'darkblue',
    'Health Insurance Fund - All Revenue' = 'light green',
    'Revenue from Employee Premiums' = 'dark green',
    'Group Insurance - 1180 & 1900' = 'blue',
    'Group Insurance-Object1180' = 'light blue'
   #     'Employer Contributions-General Fund' = 'light blue'
)) +
  labs(title="Healthcare costs and group insurance contributions", 
       caption = "Healthcare costs and group insurance contributions", 
       y = "Dollars", x = "")

Code
exp_temp <- exp_temp %>% 
  mutate(eehc = ifelse(object == "1180", 1, 0)) %>%
  mutate(eehc = ifelse((eehc == 1 & in_ff =="0"), 2, eehc))
#%>%mutate(in_ff = ifelse(eehc == 2, "1", in_ff) ) %>% filter(eehc ==2 )
table(exp_temp$eehc)

     0      1      2 
163720   4414    149 

9.4 Federal Medicaid Reimbursements and Medicaid Costs

Code
medicaid_cost <- exp_temp %>% filter(agency=="478" & (appr_org=="01" | appr_org == "65" | appr_org=="88") & (object=="4900" | object=="4400")) %>% group_by(fy) %>% summarize(sum=sum(expenditure))
  
med_reimburse <- rev_temp %>% filter(rev_type=="57" & agency=="478" & (source=="0618"|source=="2364"|source=="0660"|source=="1552"| source=="2306"| source=="2076"|source=="0676"|source=="0692")) %>% group_by(fy) %>% summarize(sum=sum(receipts))

ggplot()+
  geom_line(data=medicaid_cost, aes(x=fy, y=sum), color = "red") + 
  geom_line(data=med_reimburse, aes(x=fy, y = sum), color="black") + labs(title = "Medicaid reimbursements and Medicaid expenditures", caption = "Medicaid expenditures include funds provided to medical providers. ")

10 Expenditure & Revenue Categorization

10.1 Modify Expenditure File

10.1.1 Tax refunds

Aggregate expenditures: Save tax refunds as negative revenue. Code refunds to match the rev_type codes (02=income taxes, 03 = corporate income taxes, 06=sales tax, 09=motor fuel tax, 24=insurance taxes and fees, 35 = all other tax refunds).

Code
## negative revenue becomes tax refunds

tax_refund_long <- exp_temp %>%           # fund != "0401" # removes State Trust Funds
  filter(fund != "0401" & (object=="9910"|object=="9921"|object=="9923"|object=="9925")) %>%
  # keeps these objects which represent revenue, insurance, treasurer,and financial and professional reg tax refunds
  mutate(refund = case_when(
    fund=="0278" & sequence == "00" ~ "02", # for income tax refund
    fund=="0278" & sequence == "01" ~ "03", # tax administration and enforcement and tax operations become corporate income tax refund
     fund == "0278" & sequence == "02" ~ "02",
    object=="9921" ~ "21",                # inheritance tax and estate tax refund appropriation
    object=="9923" ~ "09",                # motor fuel tax refunds
    obj_seq_type == "99250055" ~ "06",    # sales tax refund
    fund=="0378" & object=="9925" ~ "24", # insurance privilege tax refund
    fund=="0001" & object=="9925" ~ "35", # all other taxes
      T ~ "CHECK"))                       # if none of the items above apply to the observations, then code them as CHECK 

    
exp_temp <- left_join(exp_temp, tax_refund_long) %>%
  mutate(refund = ifelse(is.na(refund),"not refund", as.character(refund)))

tax_refund <- tax_refund_long %>% 
  group_by(refund, fy)%>%
  summarize(refund_amount = sum(expenditure, na.rm = TRUE)/1000000) %>%
  pivot_wider(names_from = refund, values_from = refund_amount, names_prefix = "ref_") %>%
  mutate_all(~replace_na(.,0)) %>%
  arrange(fy)

tax_refund %>% pivot_longer( ref_02:ref_35, names_to = "Refund Type", values_to = "Amount") %>%
  ggplot()+
  geom_line(aes(x=fy,y=Amount, group = `Refund Type`, color = `Refund Type`))+
  labs(title = "Refund Types", caption = "Refunds are excluded from Expenditure totals and instead subtracted from Revenue totals") + 
  labs(title = "Tax refunds", 
       caption = "Rev_type codes: 02=income taxes, 03=corporate income taxes, 06=sales tax, 09=motor fuel tax, 
       24=insurance taxes and fees, 35 = all other tax refunds." )

Code
# remove the items we recoded in tax_refund_long
exp_temp <- exp_temp %>% filter(refund == "not refund")

tax_refund amounts are removed from expenditure totals and subtracted from revenue totals (since they were tax refunds).

10.1.2 Pension Expenditures

State payments to the following pension systems:

• Teachers Retirement System (TRS)
- New POB bond in 2019: Accelerated Bond Fund paid benefits in advance as lump sum
• State Employee Retirement System (SERS)
• State University Retirement System (SURS)
• Judges Retirement System (JRS)
• General Assembly Retirement System (GARS)

  • Includes pension stabilization fund = 0319, object = 1900 and the $300 million investment in FY2022.
  • State pension contributions are largely captured with object=4431. (State payments into pension fund)
    • includes 8 billion payment in 2004 that creates large peak in expenditure graphs
    • does not capture recent pension stabilization payments
  • Some expenditures with object=4430 are paid for with Pension obligation bond funds (fund == 0825). In past years, some POB funded expenditures were moved to revenue side in the Stata code. We are no longer doing this as of FY2021.

Modify exp_temp and move all state pension contributions to their own group (901):

Code
exp_temp <-  exp_temp %>% 
  arrange(fund) %>%
  mutate(pension = case_when( 
   (object=="4431") ~ 1, # 4431 = easy to find pension payments INTO fund
   
 # (object>"1159" & object<"1166") & fund != "0183" & fund != "0193"   ~ 2, 
   # objects 1159 to 1166 are all considered Retirement by Comptroller, 
  # Excluded - employer contributions from agencies/organizations/etc.

  (object=="1298" &  # Purchase of Investments, Normally excluded
     (fy==2010 | fy==2011) & 
     (fund=="0477" | fund=="0479" | fund=="0481")) ~ 3, #judges retirement OUT of fund
  # state borrowed money from pension funds to pay for core services during 2010 and 2011. 
  # used to fill budget gap and push problems to the future. 
 

 fund == "0319" ~ 4, # pension stabilization fund
                                        TRUE ~ 0) )

table(exp_temp$pension) 

     0      1      3      4 
167875    228      6      5 
Code
exp_temp %>% filter(pension != 0) %>%
  mutate(pension = as.factor(pension))%>%
  group_by(fy, pension) %>% 
  summarize(expenditure = sum(expenditure, na.rm = TRUE)) %>%
  ggplot(aes(x=fy, y = expenditure, group=pension)) + 
  geom_line(aes(color = pension)) + 

  labs (title = "Pension expenditures", 
  caption = "1 = State contributions INTO pension funds")+
    theme(legend.position = "bottom")

Code
# special accounting of pension obligation bond (POB)-funded contributions to JRS, SERS, GARS, TRS 

exp_temp <- exp_temp %>% 
  # change object for 2010 and 2011, retirement expenditures were bond proceeds and would have been excluded
  mutate(object = ifelse((pension >0 & in_ff == "0"), "4431", object)) %>% 
  # changes weird teacher & judge retirement system  pensions object to normal pension object 4431
  mutate(pension =  ifelse(pension >0 & in_ff == "0", 6, pension)) %>% # coded as 6 if it was supposed to be excluded. 
  mutate(in_ff = ifelse(pension>0, "1", in_ff))

table(exp_temp$pension) 

     0      1      4      6 
167875    226      5      8 
Code
# all other pensions objects  codes get agency code 901 for State Pension Contributions
exp_temp <- exp_temp %>% 
  mutate(agency = ifelse(pension>0, "901", as.character(agency)),
         agency_name = ifelse(agency == "901", "State Pension Contributions", as.character(agency_name)))

exp_temp %>% 
 filter(pension > 0) %>%  
  mutate(pension = as.factor(pension)) %>%
  group_by(fy, pension) %>% 
  summarize(expenditure = sum(expenditure, na.rm=TRUE)) %>%
  ggplot(aes(x=fy, y=expenditure, color = pension)) +
  geom_line() + 
  labs (title = "Pension Expenditures", 
  caption = "")

Code
exp_temp %>% 
 filter(pension > 0) %>%  
  group_by(fy) %>% 
  summarize(expenditure = sum(expenditure, na.rm=TRUE)) %>%
  ggplot(aes(x=fy, y=expenditure)) +
  geom_line() + 
  labs (title = "Pension Expenditures")

10.1.3 Drop Interfund transfers

  • object == 1993 is for interfund cash transfers
  • agency == 799 is for statutory transfers
  • object == 1298 is for purchase of investments and is not spending EXCEPT for costs in 2010 and 2011 (and were recoded already to object == “4431”). Over 168,000 observations remain.
Code
transfers_drop <- exp_temp %>% filter(
  agency == "799" | # statutory transfers
           object == "1993" |  # interfund cash transfers
           object == "1298") # purchase of investments

exp_temp <- anti_join(exp_temp, transfers_drop)
exp_temp
Code
transfers_drop %>% filter(fy>2019 & object == 1993) %>% group_by(obj_seq_type) %>% summarize(sum = sum(expenditure)) %>% arrange(-sum)

10.1.4 State employee healthcare costs

If observation is a group insurance contribution, then the expenditure amount is set to $0 (essentially dropped from analysis).

Code
#if observation is a group insurance contribution, then the expenditure amount is set to $0 (essentially dropped from analysis)

# pretend eehc is named group_insurance_contribution or something like that
# eehc coded as zero implies that it is group insurance
# if eehc=0, then expenditures are coded as zero for group insurance to avoid double counting costs

exp_temp <- exp_temp %>% 
  mutate(eehc = ifelse(
    # group insurance contributions for 1998-2005 and 2013-present
   fund == "0001" & (object == "1180" | object =="1900") & agency == "416" & appr_org=="20", 0, 1) )%>% 
  mutate(eehc = ifelse(
    # group insurance contributions for 2006-2012
    fund == "0001" & object == "1180" & agency == "478" & appr_org=="80", 0, eehc) )%>%
     # group insurance contributions from road fund
  # coded with 1900 for some reason??
    mutate(eehc = ifelse(
      fund == "0011" & object == "1900" & agency == "416" & appr_org=="20", 0, eehc) ) %>%
  
  mutate(expenditure = ifelse(eehc=="0", 0, expenditure)) %>%
  
  mutate(agency = case_when(   # turns specific items into State Employee Healthcare (agency=904)
      fund=="0907" & (agency=="416" & appr_org=="20") ~ "904",   # central management Bureau of benefits using health insurance reserve 
      fund=="0907" & (agency=="478" & appr_org=="80") ~ "904",   # agency = 478: healthcare & family services using health insurance reserve - stopped using this in 2012
      TRUE ~ as.character(agency))) %>%
  mutate(agency_name = ifelse(agency == "904", "STATE EMPLOYEE HEALTHCARE", as.character(agency_name)),
         in_ff = ifelse( agency == "904", 1, in_ff),
         group = ifelse(agency == "904", "904", as.character(agency)))  
# creates group variable

# Default group = agency number

healthcare_costs <- exp_temp %>% filter(group == "904")

healthcare_costs
Code
exp_temp %>% filter(group == "904") %>% group_by(fy) %>% 
  summarise(healthcare_cost = sum(expenditure, na.rm = TRUE)) %>% 
  ggplot() +geom_line(aes(x=fy, y=healthcare_cost)) + labs(title="State Employee Healthcare Costs - Included in Fiscal Futures Model", caption = "Fund 0907 for agencies responsible for health insurance reserve (DHFS & CMS)")

Code
#exp_temp <- anti_join(exp_temp, healthcare_costs) %>% mutate(expenditure = ifelse(object == "1180", 0, expenditure))

#healthcare_costs_yearly <- healthcare_costs %>% group_by(fy, group) %>% summarise(healthcare_cost = sum(expenditure, na.rm = TRUE)/1000000) %>% select(-group)

10.1.5 Local Transfers

Separate transfers to local from parent agencies that come from DOR(492) or Transportation (494). Treats muni revenue transfers as expenditures, not negative revenue.

The share of certain taxes levied state-wide at a common rate and then transferred to local governments. (Purely local-option taxes levied by specific local governments with the state acting as collection agent are NOT included.)

The six corresponding revenue items are:

• Local share of Personal Income Tax - Individual Income Tax Pass-Through New 2021 (source 2582). • Local share of General Sales Tax
• Personal Property Replacement Tax on Business Income
• Personal Property Replacement Tax on Public Utilities

• Local share of Motor Fuel Tax - Transportation Renewal Fund 0952

Until Dec 18. 2022, Local CURE was being aggregated into Revenue totals since the agency was the Department of Revenue. However the $371 million expenditure is for “LOC GOVT ARPA” and the revenue source that is Local CURE is also $371 million. Since it cancels out and is just passed through the state government, I am changing changing the fund_ab_in file so that in_ff=0 for the Local CURE fund. It also inflates the department of revenue expenditures in a misleading way when the expense is actually a transfer to local governments.

  • Dropping Local CURE fund from analysis results in a $371 million decrease in the department of Revenue (where the Local Government ARPA transfer money). The appropriation for it was over $740 million so some will probably be rolled over to FY23 too.
  • In the FY21 New and Reused Funds word document, 0325 Local CURE is described as “Created as a federal trust fund. The fund is established to receive transfers from either the disaster response and recovery fund or the state cure fund of federal funds received by the state. These transfers, subject to appropriation, will provide for the administration and payment of grants and expense reimbursements to units of local government. Revenues should be under Federal Other and expenditures under Commerce and Economic Opportunity.” - I propose changing it to exclude for both.
Code
exp_temp <- exp_temp %>% mutate(
  agency = case_when(fund=="0515" & object=="4470" & type=="08" ~ "971", # income tax to local governments
                     fund=="0515" & object=="4491" & type=="08" & sequence=="00" ~ "971", # object is shared revenue payments
                     fund=="0802" & object=="4491" ~ "972", #pprt transfer
                     fund=="0515" & object=="4491" & type=="08" & sequence=="01" ~ "976", #gst to local
                     fund=="0627" & object=="4472"~ "976" , # public transportation fund but no observations exist
                     fund=="0648" & object=="4472" ~ "976", # downstate public transportation, but doesn't exist
                     fund=="0515" & object=="4470" & type=="00" ~ "976", # object 4470 is grants to local governments
                    object=="4491" & (fund=="0188"|fund=="0189") ~ "976",
                     fund=="0187" & object=="4470" ~ "976",
                     fund=="0186" & object=="4470" ~ "976",
                    object=="4491" & (fund=="0413"|fund=="0414"|fund=="0415")  ~ "975", #mft to local
                  fund == "0952"~ "975", # Added Sept 29 2022 AWM. Transportation Renewal MFT
                    TRUE ~ as.character(agency)),
  
  agency_name = case_when(agency == "971"~ "INCOME TAX 1/10 TO LOCAL",
                          agency == "972" ~ "PPRT TRANSFER TO LOCAL",
                          agency == "975" ~ "MFT TO LOCAL",
                          agency == "976" ~ "GST TO LOCAL",
                          TRUE~as.character(agency_name)),
  group = ifelse(agency>"970" & agency < "977", as.character(agency), as.character(group)))
Code
transfers_long <- exp_temp %>% 
  filter(group == "971" |group == "972" | group == "975" | group == "976")

transfers_long %>% 
  group_by(agency_name, group, fy) %>% 
  summarize(expenditure = sum(expenditure, na.rm=TRUE) )%>% 
  ggplot() + geom_line(aes(x=fy, y = expenditure, color=agency_name)) + labs(title = "Transfers to Local Governments", caption = "Data Source: Illinois Office of the Comptroller")

Code
transfers <- transfers_long %>%
  group_by(fy, group ) %>%
  summarize(sum_expenditure = sum(expenditure)/1000000) %>%
  pivot_wider(names_from = "group", values_from = "sum_expenditure", names_prefix = "exp_" )

exp_temp <- anti_join(exp_temp, transfers_long)


dropped_inff_0 <- exp_temp %>% filter(in_ff == 0)

exp_temp <- exp_temp %>% filter(in_ff == 1) # drops in_ff = 0 funds AFTER dealing with net-revenue above

The Local Transfers from the Personal Property Replacement Tax (fund 802) increased over $2 billion from corporate income taxes alone. Personal property replacement taxes (PPRT) are revenues collected by the state of Illinois and paid to local governments to replace money that was lost by local governments when their powers to impose personal property taxes on corporations, partnerships, and other business entities were taken away.

10.1.6 Debt Service

Debt Service expenditures include interest payment on both short-term and long-term debt. We do not include escrow or principal payments.

Decision from Sept 30 2022: We are no longer including short term principal payments as a cost; only interest on borrowing is a cost. Pre FY22 and the FY21 correction, we did include an escrow payment and principle payments as costs but not bond proceeds as revenues. This caused expenditures to be inflated because we were essentially counting debt twice - the principle payment and whatever the money was spent on in other expenditure categories, which was incorrect.

Code
debt_drop <- exp_temp %>% 
  filter(object == "8841" |  object == "8811")  
# escrow  OR  principle

#debt_drop %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy)


debt_keep <- exp_temp %>% 
  filter(fund != "0455" & (object == "8813" | object == "8800" )) 
# examine the debt costs we want to include

#debt_keep %>% group_by(fy) %>% summarize(sum = sum(expenditure)) %>% arrange(-fy) 


exp_temp <- anti_join(exp_temp, debt_drop) 
exp_temp <- anti_join(exp_temp, debt_keep)

debt_keep <- debt_keep %>%
  mutate(
    agency = ifelse(fund != "0455" & (object == "8813" | object == "8800"), "903", as.character(agency)),
    group = ifelse(fund != "0455" & (object == "8813" | object == "8800"), "903", as.character(group)),
    in_ff = ifelse(group == "903", 1, as.character(in_ff)))

debt_keep_yearly <- debt_keep %>% group_by(fy, group) %>% summarize(debt_cost = sum(expenditure,na.rm=TRUE)/1000000) %>% select(-group)

10.1.7 Medicaid

Medicaid. That portion of the Healthcare and Family Services (or Public Aid in earlier years, agency code 478) budget for Medical (appr_organization code 65) for awards and grants (object codes 4400 and 4900).

State CURE will remain in the Medicaid expenditure category due to the nature of it being federal funds providing public health services and funding to locations that provide public services.

  • Uses same appropriation name of “HEALTHCARE PROVIDER RELIEF” and fund == 0793 and obj_seq_type == 49000000. So can defend the “mistake” of including healthcare provider relief as Medicaid expenditure.

Federal Medical Assistance Program (FMAP): in 1965. The FMAP formula compares the state per-capita income to the national per-capita income. There is no cap on the dollar amount that the federal government pays, so the morethat a state spends the more that it receives. a maximum of 83%. States with a higher per-capita income receive lower FMAP funding but no less than 50%, and the states that have a lower per-capita income receive higher FMAP funding. Those that need more, get more.

10.1.8 Add Other Fiscal Future group codes

Code
exp_temp <- exp_temp %>%
  #mutate(agency = as.numeric(agency) ) %>%
  # arrange(agency)%>%
  mutate(
    group = case_when(
      agency>"100"& agency<"200" ~ "910", # legislative
      
      agency == "528"  | (agency>"200" & agency<"300") ~ "920", # judicial
      pension>0  ~ "901", # pensions
      (agency>"309" & agency<"400") ~ "930",    # elected officers
      
      agency == "586" ~ "959", # create new K-12 group

      agency=="402" | agency=="418" | agency=="478" | agency=="444" | agency=="482" ~ as.character(agency), # aging, CFS, HFS, human services, public health
      T ~ as.character(group))
    ) %>%      

  
  mutate(group = case_when(
    agency=="478" & (appr_org=="01" | appr_org == "65" | appr_org=="88") & (object=="4900" | object=="4400") ~ "945", # separates CHIP from health and human services and saves it as Medicaid
    
    agency == "586" & fund == "0355" ~ "945",  # 586 (Board of Edu) has special education which is part of medicaid
    
    # OLD CODE: agency == "586" & appr_org == "18" ~ "945", # Spec. Edu Medicaid Matching
    
    agency=="425" | agency=="466" | agency=="546" | agency=="569" | agency=="578" | agency=="583" | agency=="591" | agency=="592" | agency=="493" | agency=="588" ~ "941", # public safety & Corrections
    
    agency=="420" | agency=="494" |  agency=="406" | agency=="557" ~ as.character(agency), # econ devt & infra, tollway
    
    agency=="511" | agency=="554" | agency=="574" | agency=="598" ~ "946",  # Capital improvement
    
    agency=="422" | agency=="532" ~ as.character(agency), # environment & nat. resources
    
    agency=="440" | agency=="446" | agency=="524" | agency=="563"  ~ "944", # business regulation
    
    agency=="492" ~ "492", # revenue
    
    agency == "416" ~ "416", # central management services
    agency=="448" & fy > 2016 ~ "416", #add DoIT to central management 
    
    T ~ as.character(group))) %>%
  
  
  mutate(group = case_when(
    # agency=="684" | agency=="691"  ~ as.character(agency), # moved under higher education in next line. 11/28/2022 AWM
    
    agency=="692" | agency=="695" | agency == "684" |agency == "691" | (agency>"599" & agency<"677") ~ "960", # higher education
    
    agency=="427"  ~ as.character(agency), # employment security
    
    agency=="507"|  agency=="442" | agency=="445" | agency=="452" |agency=="458" | agency=="497" ~ "948", # other departments
    
    # other boards & Commissions
    agency=="503" | agency=="509" | agency=="510" | agency=="565" |agency=="517" | agency=="525" | agency=="526" | agency=="529" | agency=="537" | agency=="541" | agency=="542" | agency=="548" |  agency=="555" | agency=="558" | agency=="559" | agency=="562" | agency=="564" | agency=="568" | agency=="579" | agency=="580" | agency=="587" | agency=="590" | agency=="527" | agency=="585" | agency=="567" | agency=="571" | agency=="575" | agency=="540" | agency=="576" | agency=="564" | agency=="534" | agency=="520" | agency=="506" | agency == "533" ~ "949", 
    
    # non-pension expenditures of retirement funds moved to "Other Departments"
    # should have removed pension expenditures already from exp_temp in Pensions step above
    agency=="131" | agency=="275" | agency=="589" |agency=="593"|agency=="594"|agency=="693" ~ "948",
    
    T ~ as.character(group))) %>%

  mutate(group_name = 
           case_when(
             group == "416" ~ "Central Management",
             group == "478" ~ "Healthcare and Family Services",
             group == "482" ~ "Public Health",
             group == "900" ~ "NOT IN FRAME",
             group == "901" ~ "STATE PENSION CONTRIBUTION",
             group == "903" ~ "DEBT SERVICE",
             group == "910" ~ "LEGISLATIVE"  ,
             group == "920" ~ "JUDICIAL" ,
             group == "930" ~ "ELECTED OFFICERS" , 
             group == "940" ~ "OTHER HEALTH-RELATED", 
             group == "941" ~ "PUBLIC SAFETY" ,
             group == "942" ~ "ECON DEVT & INFRASTRUCTURE" ,
             group == "943" ~ "CENTRAL SERVICES",
             group == "944" ~ "BUS & PROFESSION REGULATION" ,
             group == "945" ~ "MEDICAID" ,
             group == "946" ~ "CAPITAL IMPROVEMENT" , 
             group == "948" ~ "OTHER DEPARTMENTS" ,
             group == "949" ~ "OTHER BOARDS & COMMISSIONS" ,
             group == "959" ~ "K-12 EDUCATION" ,
             group == "960" ~ "UNIVERSITY EDUCATION" ,
             group == agency ~ as.character(group),
             TRUE ~ "Check name"),
         year = fy)

exp_temp %>% filter(group_name == "Check name")
Code
#write_csv(exp_temp, "all_expenditures_recoded.csv")

All expenditures recoded but not aggregated: Allows for inspection of individual expenditures within larger categories. This stage of the data is extremely useful for investigating almost all questions we have about the data.

Note that these are the raw figures BEFORE we take the additional steps:

  • Subtract tax refunds from tax revenues by revenue type.
Code
exp_temp %>% filter(fy>2020 & fund == "0561") %>% group_by(wh_approp_name, fy) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)

SBE Federal Department of Education is fund 0561. Fund 0579 is the State Board of Education.

10.2 Modify Revenue data

Revenue Categories NOT included in Fiscal Futures:
- 32. Garnishment-Levies. (State is fiduciary, not beneficiary.)
- 45. Student Fees-Universities. (Excluded from state-level budget.)
- 51. Retirement Contributions (of individuals and non-state entities).
- 66. Proceeds, Investment Maturities. (Not sustainable flow.)
- 72. Bond Issue Proceeds. (Not sustainable flow.)
- 75. Inter-Agency Receipts.
- 79. Cook County Intergovernmental Transfers. (State is not beneficiary.)
- 98. Prior Year Refunds.
- 99. Statutory Transfers.

All Other Sources

Expanded to include the following smaller sources:
- 30. Horse Racing Taxes & Fees.
- 60. Other Grants and Contracts.
- 63. Investment Income.

For aggregating revenue, use the rev_1998_2022 dataframe, join the funds_ab_in_2022 file to it, and then join the ioc_source_type file to the dataset. Remember: You need to update the funds_ab_in and ioc_source_type file every year!

Code
# recodes old agency numbers to consistent agency number
rev_temp <- rev_temp %>% 
  mutate(agency = case_when(
    (agency=="438"| agency=="475" |agency == "505") ~ "440",
    # financial institution &  professional regulation &
     # banks and real estate  --> coded as  financial and professional reg
    agency == "473" ~ "588", # nuclear safety moved into IEMA
    (agency =="531" | agency =="577") ~ "532", # coded as EPA
    (agency =="556" | agency == "538") ~ "406", # coded as agriculture
    agency == "560" ~ "592", # IL finance authority (fire trucks and agriculture stuff)to state fire marshal
    agency == "570" & fund == "0011" ~ "494",   # city of Chicago road fund to transportation
    TRUE ~ (as.character(agency)))) 

10.2.1 Federal to State Transfers

Code
#rev_temp <- rev_temp %>% filter(in_ff==1)

rev_temp <- rev_temp %>% 
  mutate(
    rev_type = ifelse(rev_type=="57" & agency=="478" & (source=="0618"|source=="2364"|source=="0660"|source=="1552"| source=="2306"| source=="2076"|source=="0676"|source=="0692"), "58", rev_type),
    rev_type_name = ifelse(rev_type=="58", "Federal Medicaid Reimbursements", rev_type_name),
    rev_type = ifelse(rev_type=="57" & agency=="494", "59", rev_type),
    rev_type_name = ifelse(rev_type=="59", "Federal Transportation", rev_type_name),
    rev_type_name = ifelse(rev_type=="57", "Federal - Other", rev_type_name),
    rev_type = ifelse(rev_type=="6", "06", rev_type),
    rev_type = ifelse(rev_type=="9", "09", rev_type)) 

rev_temp %>% 
  filter(rev_type == "58" | rev_type == "59" | rev_type == "57") %>% 
  group_by(fy, rev_type, rev_type_name) %>% 
  summarise(receipts = sum(receipts, na.rm = TRUE)/1000000) %>% 
  ggplot() +
  geom_line(aes(x=fy, y=receipts,color=rev_type_name)) +
      theme_bw() +
  scale_y_continuous(labels = comma)+
  labs(title = "Federal to State Transfers", 
       y = "Millions of Dollars", x = "") + 
  theme(legend.position = "bottom", legend.title = element_blank()  )

Looking at Federal Revenue:

All revenue sources within “Federal - Other” source.

Code
rev_temp %>% filter(rev_type == "57" & fy >2018) %>% group_by(fund_name, source_name_AWM,  fy) %>% summarize(receipts =sum(receipts)) %>% arrange(-receipts) %>% pivot_wider(names_from = fy, values_from = receipts)
Code
fed_rev_compare <- rev_temp %>% filter((rev_type == "57" | rev_type == "58" | rev_type == "59") & (fy == 2022 | fy==2021 | fy==2020 | fy == 2019)) %>%  arrange(-receipts)
write_csv(fed_rev_compare, "comparefedrev.csv")


rev_temp %>% filter(source_name_AWM == "FEDERAL STIMULUS PACKAGE") %>% group_by(fy, fund_name) %>% summarize(receipts =sum(receipts)) %>% arrange(-fy)
Code
rev_temp %>% filter(fy > 2018 & source_name_AWM == "FEDERAL STIMULUS PACKAGE") %>% group_by(fund_name, fy) %>% summarize(receipts =sum(receipts)) %>% arrange(-receipts)
Code
rev_temp %>% filter(rev_type == "57" & fy > 2018 & fund_name == "SBE FEDERAL DEPT OF EDUCATION") %>% group_by(source_name_AWM , fund_name, fy) %>% summarize(receipts =sum(receipts)) %>% arrange(-receipts)
Code
exp_temp %>% filter(fy >2019 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE" | fund_name == "SBE FEDERAL DEPT OF EDUCATION" | fund_name == "DISASTER RESPONSE AND RECOVERY" | fund_name == "ESSENTIAL GOVT SERV SUPPORT" )) %>% group_by(fy, agency_name, wh_approp_name, fund_name) %>% 
  summarize(sum=sum(expenditure),
            appropriated = sum(appn_net_xfer)) %>% 
  arrange(-appropriated)
Code
exp_temp %>% filter(fy >2019 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE" | fund_name == "SBE FEDERAL DEPT OF EDUCATION" | fund_name == "DISASTER RESPONSE AND RECOVERY" | fund_name == "ESSENTIAL GOVT SERV SUPPORT" )) %>% group_by(fy, wh_approp_name, fund_name) %>% 
  summarize(sum=sum(expenditure),
            appropriated = sum(appn_net_xfer)) %>% 
  arrange(-appropriated)
Code
exp_temp %>% filter(fy >2019 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE" | fund_name == "SBE FEDERAL DEPT OF EDUCATION" | fund_name == "DISASTER RESPONSE AND RECOVERY" | fund_name == "ESSENTIAL GOVT SERV SUPPORT" )) %>% group_by(fund_name, fy, agency_name) %>% 
  summarize(sum=sum(expenditure),
            appropriated = sum(appn_net_xfer)) %>% 
  arrange(-appropriated)
Code
exp_temp %>% filter(fy == 2022 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(org_name, agency_name, object, wh_approp_name, fund_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)
Code
exp_temp %>% filter(fy == 2022 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(agency_name, object, wh_approp_name, fund_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)
Code
exp_temp %>% filter(fy == 2022 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(fund_name, object, org_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)
Code
exp_temp %>% filter(fy == 2022 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(fund_name, agency_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)
Code
exp_temp %>% filter(fy == 2022 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(agency_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)
Code
exp_temp %>% filter(fy == 2021 & (fund_name == "STATE CURE" | fund_name == "LOCAL CURE")) %>% group_by(wh_approp_name, fund_name) %>% summarize(sum=sum(expenditure)) %>% arrange(-sum)

Dropping State CURE Revenue

The Fiscal Futures model focuses on sustainable revenue sources. To understand our fiscal gap and outlook, we need to exclude these one time revenues. GOMB has emphasized that they have allocated COVID dollars to one time expenditures (unemployment trust fund, budget stabilization fund, etc.). The fiscal gap, graphs,and CAGRs have been recalculated in the Drop COVID Dollars section below. In addition, an attempt at forecasting revenue and expenditures is also made after dropping the federal COVID dollars.

NOTE: I have only dropped State and Local CURE revenue so far. Federal money went into other funds during the beginning of pandemic.

Code
rev_temp <- rev_temp %>% mutate(covid_dollars = ifelse(source_name_AWM == "FEDERAL STIMULUS PACKAGE",1,0))

10.2.2 Health Insurance Premiums from Employees

Insurance premiums for employees is coded below but it is NOT used in the fiscal futures model. Employee and employer premiums are considered rev_51 and dropped from analysis in later step.

0120 = ins prem-option life
0120 = ins prem-optional life/univ

0347 = optional health - HMO
0348 = optional health - dental
0349 = optional health - univ/local SI
0350 = optional health - univ/local
0351 = optional health - retirement
0352 = optional health - retirement SI
0353 = optional health - retire/dental
0354 = optional health - retirement hmo

2199-2209 = various HMOs, dental, health plans from Health Insurance Reserve (fund)

Code
#collect optional insurance premiums to fund 0907 for use in eehc expenditure  
rev_temp <- rev_temp %>% 
  mutate(
    #variable not used in aggregates, but could be interesting for other purposes
    employee_premiums = ifelse(fund=="0907" & (source=="0120"| source=="0121"| (source>"0345" & source<"0357")|(source>"2199" & source<"2209")), 1, 0),
    
    # adds more rev_type codes
    rev_type = case_when(
      fund =="0427" ~ "12", # pub utility tax
      fund == "0742" | fund == "0473" ~ "24", # insurance and fees
      fund == "0976" ~ "36",# receipts from rev producing
      fund == "0392" |fund == "0723" ~ "39", # licenses and fees
      fund == "0656" ~ "78", #all other rev sources
      TRUE ~ as.character(rev_type)))
# if not mentioned, then rev_type as it was



# # optional insurance premiums = employee insurance premiums

# emp_premium <- rev_temp %>%
#   group_by(fy, employee_premiums) %>%
#   summarize(employee_premiums_sum = sum(receipts)/1000000) %>%
#   filter(employee_premiums == 1) %>%
#   rename(year = fy) %>% 
#   select(-employee_premiums)

emp_premium_long <- rev_temp %>%  filter(employee_premiums == 1)
# 381 observations have employee premiums == 1


# drops employee premiums from revenue
# rev_temp <- rev_temp %>% filter(employee_premiums != 1)
# should be dropped in next step since rev_type = 51

Employee premiums are dropped in the following steps. In FY21, employee premiums were subtracted from state healthcare costs on the expenditure side to calculate a “Net Healthcare Cost” but that methodology has been discontinued. Totals were practically unchanged: revenue from employee premiums is also very small.

10.2.3 Transfers in and Out:

Funds that hold and disperse local taxes or fees are dropped from the analysis. Then other excluded revenue types are also dropped.

Drops Blank, Student Fees, Retirement contributions, proceeds/investments, bond issue proceeds, interagency receipts, cook IGT, Prior year refunds:

I don’t have much faith in the transfers in and out steps- AWM

I am currently choosing to exclude the totals from this step. Overall, this decreases the total revenues in “All Other Revenues” by a few million dollars.

  • in_from_out <- c(“0847”, “0867”, “1175”, “1176”, “1177”, “1178”, “1181”, “1182”, “1582”, “1592”, “1745”, “1982”, “2174”, “2264”)
  • See the methodology document for the list of what these sources/funds are.
Code
rev_temp <- rev_temp %>% 
  filter(in_ff == 1) %>% 
  mutate(local = ifelse(is.na(local), 0, local)) %>% # drops all revenue observations that were coded as "local == 1"
  filter(local != 1)

# 1175 doesnt exist?
in_from_out <- c("0847", "0867", "1175", "1176", "1177", "1178", "1181", "1182", "1582", "1592", "1745", "1982", "2174", "2264")

# what does this actually include:
# all are items with rev_type = 75 originally. 
in_out_df <- rev_temp %>%
  mutate(infromout = ifelse(source %in% in_from_out, 1, 0)) %>%
  filter(infromout == 1)

rev_temp <- rev_temp %>% 
  mutate(rev_type_new = ifelse(source %in% in_from_out, "76", rev_type))
# if source contains any of the codes in in_from_out, code them as 76 (all other rev).
# I end up excluding rev_76 in later steps

Corporate income tax Individual Income Tax Pass-Through (source =2582) was over 2 billion. The PTE tax allows a workaround to the federal $10,000 limitation for state and local tax (SALT) deductions and expires Jan 1. 2026 (to correspond with remaining years that the Tax Cuts and Jobs Act SALT limitation is in effect) Tax Adviser. With the enactment of the Tax Cuts and Jobs Act of 2017 (“TCJA”), individual taxpayers were limited to a $10,000 state and local tax deduction per year. In response to this limitation, many states created a workaround mechanism, introducing a pass-through entity tax (“PTET”). This shifted the state and local tax deduction from an individual taxpayer to the entity level that is not subject to the $10,000 limitation. Implications: Illinois residents in multistate passthrough entities will need to pay estimated taxes on income that is not subject to the SALT cap tax. TCJA of 2017 decreased

Code
# revenue types to drop
drop_type <- c("32", "45", "51", 
               "66", "72", "75", "79", "98")

# drops Blank, Student Fees, Retirement contributions, proceeds/investments,
# bond issue proceeds, interagency receipts, cook IGT, Prior year refunds.


rev_temp <- rev_temp %>% filter(!rev_type_new %in% drop_type)
# keep observations that do not have a revenue type mentioned in drop_type

table(rev_temp$rev_type_new)

   02    03    06    09    12    15    18    21    24    27    30    31    33 
  161   124   828   127   575   258    45  1420   450    76   659   124   130 
   35    36    39    42    48    54    57    58    59    60    63    76    78 
  660  5152  9044  2755    31  1239  6451   620   226   103  5081   154 11261 
   99 
  964 
Code
rev_temp %>% 
  group_by(fy, rev_type_new) %>% 
  summarize(total_reciepts = sum(receipts)/1000000) %>%
  pivot_wider(names_from = rev_type_new, values_from = total_reciepts, names_prefix = "rev_") 
Code
# combines smallest 4  categories to to "Other"
# they were the 4 smallest in past years, are they still the 4 smallest? 

rev_temp <- rev_temp %>%  
 mutate(rev_type_new = ifelse(rev_type=="30" | rev_type=="60" | rev_type=="63" | rev_type=="76", "78", rev_type_new))


#table(rev_temp$rev_type_new)  # check work



rm(rev_1998_2022)
rm(exp_1998_2022)


#write.csv(exp_temp, "exp_fy22_recoded_12192022.csv")
#write.csv(rev_temp, "rev_fy22_recoded_12192022.csv")

10.3 Pivoting and Merging

  • Local Government Transfers (exp_970) should be on the expenditure side

  • State employer group insurance contributions should be dropped to avoid double counting both the state. Do not do this. This was done for FY21 only and will not be done again.

  • Subtract employee insurance premiums from State Employee Healthcare Expenditures (group == 904) - Employee Premiums = Actual state healthcare costs.

  • ff_exp\(exp904 − emp_premium\)employee_premiums_sum = statehealthcarecosts

    • Did in FY21, but not doing again. Minor difference in fiscal gap overall from change in methodology.

10.3.1 Revenues

I chose to drop rev_76 for Transfers in and Out because I do not understand why that step occurs in the previously used Stata code. Rev_76 was created and included in rev_78 for All Other Revenues in old Stata code for years before FY21 but that method has been discontinued for FY22.

Code
ff_rev <- rev_temp %>% 
  group_by(rev_type_new, fy) %>% 
  summarize(sum_receipts = sum(receipts, na.rm=TRUE)/1000000 ) %>%
  pivot_wider(names_from = "rev_type_new", values_from = "sum_receipts", names_prefix = "rev_")

ff_rev<- left_join(ff_rev, tax_refund)

#ff_rev <- left_join(ff_rev, pension2_fy22, by=c("fy" = "year"))

#ff_rev <- left_join(ff_rev, eehc2_amt) 
ff_rev <- mutate_all(ff_rev, ~replace_na(.,0))


ff_rev <- ff_rev %>%
  mutate(rev_02 = rev_02 - ref_02,
         rev_03 = rev_03 - ref_03,
         rev_06 = rev_06 - ref_06,
         rev_09 = rev_09 - ref_09,
         rev_21 = rev_21 - ref_21,
         rev_24 = rev_24 - ref_24,
         rev_35 = rev_35 - ref_35

      #   rev_78new = rev_78 #+ pension_amt #+ eehc
         ) %>% 
  select(-c(ref_02:ref_35, rev_99, rev_NA, rev_76#, pension_amt , rev_76,
          #  , eehc
            ))

ff_rev

Since I already pivot_wider()ed the table in the previous code chunk, I now change each column’s name by using rename() to set new variable names. Ideally the final dataframe would have both the variable name and the variable label but I have not done that yet.

Code
aggregate_rev_labels <- ff_rev %>%
  rename("INDIVIDUAL INCOME TAXES, gross of local, net of refunds" = rev_02,
         "CORPORATE INCOME TAXES, gross of PPRT, net of refunds" = rev_03,
         "SALES TAXES, gross of local share" = rev_06 ,
         "MOTOR FUEL TAX, gross of local share, net of refunds" = rev_09 ,
         "PUBLIC UTILITY TAXES, gross of PPRT" = rev_12,
         "CIGARETTE TAXES" = rev_15 ,
         "LIQUOR GALLONAGE TAXES" = rev_18,
         "INHERITANCE TAX" = rev_21,
         "INSURANCE TAXES&FEES&LICENSES, net of refunds" = rev_24 ,
         "CORP FRANCHISE TAXES & FEES" = rev_27,
       # "HORSE RACING TAXES & FEES" = rev_30,  # in Other
         "MEDICAL PROVIDER ASSESSMENTS" = rev_31 ,
         # "GARNISHMENT-LEVIES " = rev_32 , # dropped
         "LOTTERY RECEIPTS" = rev_33 ,
         "OTHER TAXES" = rev_35,
         "RECEIPTS FROM REVENUE PRODUCNG" = rev_36, 
         "LICENSES, FEES & REGISTRATIONS" = rev_39 ,
         "MOTOR VEHICLE AND OPERATORS" = rev_42 ,
         #  "STUDENT FEES-UNIVERSITIES" = rev_45,   # dropped
         "RIVERBOAT WAGERING TAXES" = rev_48 ,
         # "RETIREMENT CONTRIBUTIONS " = rev_51, # dropped
         "GIFTS AND BEQUESTS" = rev_54, 
         "FEDERAL OTHER" = rev_57 ,
         "FEDERAL MEDICAID" = rev_58, 
         "FEDERAL TRANSPORTATION" = rev_59 ,
         #"OTHER GRANTS AND CONTRACTS" = rev_60, #other
       # "INVESTMENT INCOME" = rev_63, # other
         # "PROCEEDS,INVESTMENT MATURITIES" = rev_66 , #dropped
         # "BOND ISSUE PROCEEDS" = rev_72,  #dropped
         # "INTER-AGENCY RECEIPTS" = rev_75,  #dropped
      #  "TRANSFER IN FROM OUT FUNDS" = rev_76,  #other
         "ALL OTHER SOURCES" = rev_78,
         # "COOK COUNTY IGT" = rev_79, #dropped
         # "PRIOR YEAR REFUNDS" = rev_98 #dropped
  ) 

aggregate_rev_labels

10.3.2 Expenditures

Create exp_970 for all local government transfers (exp_971 + exp_972 + exp_975 + exp_976).

Create state employee healthcare costs that reflects the health costs minus the optional insurance premiums that came in (904_new=904−med_option_amt_recent). Do not do this. This was done for FY21 only and will not be done again. Small differences in overall Fiscal Gap from methodology change.

Code
ff_exp <- exp_temp %>% 
  group_by(fy, group) %>% 
  summarize(sum_expenditures = sum(expenditure, na.rm=TRUE)/1000000 ) %>%
  pivot_wider(names_from = "group", values_from = "sum_expenditures", names_prefix = "exp_")%>%
  
    left_join(debt_keep_yearly) %>%
  mutate(exp_903 = debt_cost) %>%

  #  left_join(healthcare_costs_yearly) %>%

  # join state employee healthcare and subtract employee premiums
  # left_join(emp_premium, by = c("fy" = "year")) %>%
#  mutate(exp_904_new = (`healthcare_cost` - `employee_premiums_sum`)) %>% # state employee healthcare premiums
  
 # left_join(retirement_contributions) %>%
  #    mutate(exp_901_new = exp_901 - contributions/1000000) %>% #employee pension contributions


  # join local transfers and create exp_970
  left_join(transfers) %>%
  mutate(exp_970 = exp_971 + exp_972  + exp_975 + exp_976)

ff_exp<- ff_exp %>% select(-c(debt_cost, exp_971:exp_976)) # drop unwanted columns
ff_exp

11 Clean Table Outputs

Create total revenues and total expenditures only:

  • after aggregating expenditures and revenues, pivoting wider, then I want to drop the columns that I no longer want and then pivot_longer(). After pivoting_longer() and creating rev_long and exp_long, expenditures and revenues are in the same format and can be combined together for the totals and gap each year.
Code
rev_long <- pivot_longer(ff_rev, rev_02:rev_78, names_to = c("type","Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy) %>%
  mutate(Category_name = case_when(
    Category == "02" ~ "INDIVIDUAL INCOME TAXES" ,
    Category == "03" ~ "CORPORATE INCOME TAXES" ,
    Category == "06" ~ "SALES TAXES" ,
    Category == "09" ~ "MOTOR FUEL TAX" ,
    Category == "12" ~ "PUBLIC UTILITY TAXES" ,
    Category == "15" ~ "CIGARETTE TAXES" ,
    Category == "18" ~ "LIQUOR GALLONAGE TAXES" ,
    Category == "21" ~ "INHERITANCE TAX" ,
    Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES" ,
    Category == "27" ~ "CORP FRANCHISE TAXES & FEES" ,
    Category == "30" ~ "HORSE RACING TAXES & FEES",  # in Other
    Category == "31" ~ "MEDICAL PROVIDER ASSESSMENTS" ,
    Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped
    Category == "33" ~  "LOTTERY RECEIPTS" ,
    Category == "35" ~  "OTHER TAXES" ,
    Category == "36" ~  "RECEIPTS FROM REVENUE PRODUCING", 
    Category == "39" ~  "LICENSES, FEES & REGISTRATIONS" ,
    Category == "42" ~  "MOTOR VEHICLE AND OPERATORS" ,
    Category == "45" ~  "STUDENT FEES-UNIVERSITIES",   # dropped
    Category == "48" ~  "RIVERBOAT WAGERING TAXES" ,
    Category == "51" ~  "RETIREMENT CONTRIBUTIONS" , # dropped
    Category == "54" ~ "GIFTS AND BEQUESTS", 
    Category == "57" ~  "FEDERAL OTHER" ,
    Category == "58" ~  "FEDERAL MEDICAID", 
    Category == "59" ~  "FEDERAL TRANSPORTATION" ,
    Category == "60" ~  "OTHER GRANTS AND CONTRACTS", #other
    Category == "63" ~  "INVESTMENT INCOME", # other
    Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped
    Category == "72" ~ "BOND ISSUE PROCEEDS",  #dropped
    Category == "75" ~  "INTER-AGENCY RECEIPTS ",  #dropped
    Category == "76" ~  "TRANSFER IN FROM OUT FUNDS",  #other
    Category == "78" ~  "ALL OTHER SOURCES" ,
    Category == "79" ~   "COOK COUNTY IGT", #dropped
    Category == "98" ~  "PRIOR YEAR REFUNDS", #dropped
                 T ~ "Check Me!"

  ) )%>% 
  mutate(Category_name = str_to_title(Category_name))


exp_long <- pivot_longer(ff_exp, exp_402:exp_970 , names_to = c("type", "Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy ) %>% 
  mutate(Category_name = 
           case_when(
             Category == "402" ~ "AGING" ,
             Category == "406" ~ "AGRICULTURE", 
             Category == "416" ~ "CENTRAL MANAGEMENT",
             Category == "418" ~ "CHILDREN AND FAMILY SERVICES", 
             Category == "420" ~ "COMMERCE AND ECONOMIC OPPORTUNITY",
             Category == "422" ~ "NATURAL RESOURCES" ,
             Category == "426" ~ "CORRECTIONS",
             Category == "427" ~ "EMPLOYMENT SECURITY" ,
             Category == "444" ~ "HUMAN SERVICES" ,
             Category == "448" ~ "Innovation and Technology", # AWM added fy2022
             Category == "478" ~ "FAMILY SERVICES net Medicaid", 
             Category == "482" ~ "PUBLIC HEALTH", 
             Category == "492" ~ "REVENUE", 
             Category == "494" ~ "TRANSPORTATION" ,
             Category == "532" ~ "ENVIRONMENTAL PROTECT AGENCY" ,
             Category == "557" ~ "IL STATE TOLL HIGHWAY AUTH" ,
             Category == "684" ~ "IL COMMUNITY COLLEGE BOARD", 
             Category == "691" ~ "IL STUDENT ASSISTANCE COMM" ,
             Category == "900" ~ "NOT IN FRAME",
             Category == "901" ~ "STATE PENSION CONTRIBUTION",
             Category == "903" ~ "DEBT SERVICE",
             Category == "904" ~ "State Employee Healthcare",
             Category == "910" ~ "LEGISLATIVE"  ,
             Category == "920" ~ "JUDICIAL" ,
             Category == "930" ~ "ELECTED OFFICERS" , 
             Category == "940" ~ "OTHER HEALTH-RELATED", 
             Category == "941" ~ "PUBLIC SAFETY" ,
             Category == "942" ~ "ECON DEVT & INFRASTRUCTURE" ,
             Category == "943" ~ "CENTRAL SERVICES",
             Category == "944" ~ "BUS & PROFESSION REGULATION" ,
             Category == "945" ~ "MEDICAID" ,
             Category == "946" ~ "CAPITAL IMPROVEMENT" , 
             Category == "948" ~ "OTHER DEPARTMENTS" ,
             Category == "949" ~ "OTHER BOARDS & COMMISSIONS" ,
             Category == "959" ~ "K-12 EDUCATION" ,
             Category == "960" ~ "UNIVERSITY EDUCATION",
             Category == "970" ~ "Local Govt Transfers",
             T ~ "CHECK ME!")
           ) %>% 
  mutate(Category_name = str_to_title(Category_name))

#write_csv(exp_long, "expenditures_recoded_long_FY22.csv")
#write_csv(rev_long, "revenue_recoded_long_FY22.csv")

aggregated_totals_long <- rbind(rev_long, exp_long)
aggregated_totals_long
Code
year_totals <- aggregated_totals_long %>% 
  group_by(type, Year) %>% 
  summarize(Dollars = sum(Dollars, na.rm = TRUE)) %>% 
  pivot_wider(names_from = "type", values_from = Dollars) %>% 

  rename(
         Expenditures = exp,
         Revenue = rev) %>%  
  mutate(`Fiscal Gap` = round(Revenue - Expenditures))
# %>%  arrange(desc(Year))
# creates variable for the Gap each year

year_totals  %>%  
  kbl(caption = "Fiscal Gap for each Fiscal Year") %>% 
  kable_styling(bootstrap_options = c("striped"))  %>%
kable_classic() %>%   add_footnote(c("Methodology has changed since past publications","Values include State and Local CURE dollars"))
Fiscal Gap for each Fiscal Year
Year Expenditures Revenue Fiscal Gap
1998 31218.46 31264.68 46
1999 33804.97 33030.25 -775
2000 37283.05 35846.01 -1437
2001 40300.24 37147.74 -3153
2002 42014.32 36825.93 -5188
2003 42567.14 36805.70 -5761
2004 52980.21 40856.24 -12124
2005 45331.22 42865.86 -2465
2006 48028.45 44700.58 -3328
2007 51098.60 48033.25 -3065
2008 54138.64 50213.48 -3925
2009 56721.05 49858.93 -6862
2010 59247.72 49838.70 -9409
2011 60403.66 54731.97 -5672
2012 59831.15 56248.10 -3583
2013 63261.02 60804.22 -2457
2014 66941.54 62772.24 -4169
2015 69920.58 64113.56 -5807
2016 63909.28 61985.56 -1924
2017 71704.79 61349.21 -10356
2018 74942.57 70465.15 -4477
2019 74383.60 72152.87 -2231
2020 81574.31 78141.69 -3433
2021 93027.93 91806.06 -1222
2022 102219.16 113392.66 11173
a Methodology has changed since past publications
b Values include State and Local CURE dollars

12 Graphs

Graphs made from aggregated_totals_long dataframe.

Code
annotation <- data.frame(
  x = c(2004, 2017, 2019),
  y = c(60000, 50000, 5000),  
  label = c("Expenditures","Revenue", "Fiscal Gap")
)

# with trend lines:
year_totals %>%  
  ggplot() +
  # geom_smooth adds regression line, graphed first so it appears behind line graph
  geom_smooth(aes(x = Year, y = Revenue), color = "rosybrown2", alpha = 0.7, method = "lm", se = FALSE) + 
  geom_smooth(aes(x = Year, y = Expenditures), color = "gray", method = "lm", se = FALSE) +
  
  # line graph of revenue and expenditures
  geom_line(aes(x = Year, y = Revenue), color = "Black", size=1) +
  geom_line(aes(x = Year, y = Expenditures), color = "red", size=1) +
  
  # labels
    theme_bw() +
  scale_y_continuous(labels = comma)+
  xlab("Year") + 
  ylab("Millions of Dollars")  +
  ggtitle("Illinois Expenditures and Revenue Totals, 1998-2022")

Code
# without trend lines:
year_totals %>%  
  ggplot() +
  # line graph of revenue and expenditures
  geom_line(aes(x = Year, y = Revenue), color = "Black", size=1) +
  geom_line(aes(x = Year, y = Expenditures), color = "red", size=1) +
      theme_bw() +
  scale_y_continuous(labels = comma)+
  xlab("Year") + 
  ylab("Millions of Dollars")  +
  ggtitle("Illinois Expenditures and Revenue Totals, 1998-2022")

Code
fiscal_gap <- year_totals %>%  
  ggplot() +
  geom_hline(yintercept = 0) +
  # geom_smooth adds regression line, graphed first so it appears behind line graph
  geom_smooth(aes(x = Year, y = Revenue), color = "gray", alpha = 0.7, method = "lm", se = FALSE) + 
  #  scale_linetype_manual(values="dashed")+
  geom_smooth(aes(x = Year, y = Expenditures), color ="rosybrown2", linetype = "dashed", method = "lm", se = FALSE, alpha = 0.7) +

  # line graph of revenue and expenditures
  geom_line(aes(x = Year, y = Revenue), color = "Black", size=1) +
  geom_line(aes(x = Year, y = Expenditures, linetype = "dashed"), color = "red", lwd=1) +
  geom_line(aes(x = Year, y = `Fiscal Gap`), color = "gray") +
  
  geom_text(data = annotation, aes(x=x, y=y, label=label))+
  # labels
    theme_bw() +
    theme(legend.position = "none")+

    scale_linetype_manual(values = c("dashed", "dashed")) +

  scale_y_continuous(labels = comma)+
  xlab("Year") + 
  ylab("Millions of Dollars")  +
  ggtitle("Illinois Expenditures and Revenue Totals, 1998-2022")

fiscal_gap

Code
annotation_billions <- data.frame(
  x = c(2004, 2017, 2019),
  y = c(60, 50, 5),  
  label = c("Expenditures","Revenue", "Fiscal Gap")
)
fiscal_gap2 <- year_totals %>%  
  ggplot() +
  geom_hline(yintercept = 0) +
  geom_line(aes(x = Year, y = Revenue/1000), color = "Black", lwd=1) +
  geom_line(aes(x = Year, y = Expenditures/1000, linetype = "dashed"), color = "red", lwd=1) +
  geom_line(aes(x = Year, y = `Fiscal Gap`/1000), color = "gray") +
  
  geom_text(data = annotation, aes(x=x, y=y/1000, label=label))+
    theme_bw() +
  theme(legend.position = "none")+
    scale_linetype_manual(values = c("dashed", "dashed")) +

  scale_y_continuous(labels = comma)+
  xlab("Year") + 
  ylab("Billions of Dollars")  +
  ggtitle("Illinois Expenditures and Revenue Totals, 1998-2022")

fiscal_gap2

Code
aggregated_totals_long %>%  
  filter(type == "exp") %>% # uses only expenditures
  ggplot(aes(x = Year, y = Dollars, group = Category, color = Category)) +
  geom_line()+
    xlab("Year") + 
    ylab("Millions of Dollars")  +
    ggtitle("Illinois Expenditures by Category")

Code
aggregated_totals_long %>%  
  filter(type == "rev") %>% #uses only revenues
  ggplot(aes(x = Year, y = Dollars, group = Category, color = Category)) +
  geom_line()+
    xlab("Year") + 
    ylab("Millions of Dollars")  +
    ggtitle("Illinois Revenues by Category")

Expenditure and revenue amounts in millions of dollars:

Code
exp_long %>%
  filter(Year == 2022) %>%
  #mutate(`Total Expenditures`= sum(Dollars, na.rm = TRUE)) %>%
 # select(-c(Year, `Total Expenditures`)) %>%
  arrange(desc(`Dollars`)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`, fill = "red"))+ 
  coord_flip() +
      theme_bw()+
  theme(legend.position = "none") +
  labs(title = "Expenditures for FY2022") +
    xlab("Expenditure Categories") +
  ylab("Millions of Dollars") 

Code
rev_long %>%
  filter(Year == 2022) %>%
  #mutate(`Total Expenditures`= sum(Dollars, na.rm = TRUE)) %>%
 # select(-c(Year, `Total Expenditures`)) %>%
  arrange(desc(`Dollars`)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`))+ 
  coord_flip() +
    theme_bw() +
    theme(legend.position = "none") +
      labs(title = "Revenues for FY2022")+
    xlab("Revenue Categories") +
  ylab("Millions of Dollars") 

Expenditure and revenues when focusing on largest categories and combining others into “All Other Expenditures(Revenues)”:

Code
exp_long %>%
  filter( Year == 2022) %>%
  mutate(rank = rank(Dollars),
        Category_name = ifelse(rank > 13, Category_name, 'All Other Expenditures')) %>%
 # select(-c(Year, Dollars, rank)) %>%
  arrange(desc(Dollars)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`), fill = "rosybrown2")+ 
  coord_flip() +
      theme_bw() +
    labs(title = "Expenditures for FY2022") +
    xlab("") +
  ylab("Millions of Dollars")

Code
rev_long %>%
  filter( Year == 2022) %>%
  mutate(rank = rank(Dollars),
        Category_name = ifelse(rank > 10, Category_name, 'All Other Sources')) %>%
  arrange(desc(Dollars)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`), fill = "dark gray")+ 
  coord_flip() +
      theme_bw() +
    labs(title = "Revenues for FY2022") +
    xlab("") +
  ylab("Millions of Dollars")

Changes in Categories - 2021 to 2022 Dot Plot Attempt:

Code
rev_long %>%
    filter(Year == "2022" | Year == "2021") %>%
  mutate(Year = as.character(Year)) %>%
  ggplot(aes(x = Dollars, y = reorder(Category, Dollars))) +
  geom_line(aes(group = Category) )+
    geom_text(aes(x = ifelse(Year == "2022", as.numeric(Dollars), NA),  label = ifelse(Year == "2022", Category_name, "")),  
            hjust = -0.2,
            size = 2.8) +
         geom_point(aes(color = Year), size=2)  +
  labs(title = "2021 to 2022 Change in Revenue", x = "Millions of Dollars" , y = "",  caption = "")  +
   scale_fill_manual(values = c("#d62828", "#003049"), labels = c("FY 2021", "FY 2022"))+
    scale_color_manual(values = c("#d62828", "#003049")) +   
  theme_classic()+ 
    theme(
   legend.position = "bottom" ,
  axis.text.y = element_blank(),
  axis.ticks.y = element_blank(),
  axis.line.y.left  = element_blank(),
 # axis.line.x = element_blank(),
  #  axis.title.y = element_blank(),
 # axis.ticks.x = element_blank()
 )+
  scale_x_continuous(limits = c(0, 31000), labels = comma)

Code
exp_long %>%
    filter(Year == "2022" | Year == "2021") %>%
  mutate(Year = as.character(Year)) %>%
  ggplot(aes(x = Dollars, y = reorder(Category, Dollars))) +
  geom_line(aes(group = Category) )+
  geom_text(aes(x = ifelse(Year == "2022", (as.numeric(Dollars)+1100), NA),  
                label = ifelse(Year == "2022", Category_name, "")),  
            hjust = 0,
            size = 2.8) +
  geom_point(aes(color = Year), size=2 #, alpha = 0.5
             )  +
  labs(title = "2021 to 2022 Change in Expenditures", x = "Millions of Dollars" , y = "",  caption = "")  +
   scale_fill_manual(values = c("#d62828", "#003049"), labels = c("FY 2021", "FY 2022"))+
    scale_color_manual(values = c("#d62828", "#003049")) +

   theme_classic()+ 
    theme(
    legend.position = "bottom" ,
  axis.text.y = element_blank(),
  axis.ticks.y = element_blank(),
  axis.line.y.left  = element_blank(),
  #axis.line.x = element_blank(),
   # axis.title.y = element_blank(),
  #axis.ticks.x = element_blank()
  )+
  scale_x_continuous(limits = c(0, 31000), labels = comma)

12.0.1 Top 3 Revenues

Code
annotation <- data.frame(
  x = c(2012, 2019, 2012),
  y = c(16000, 10000, 5000),  
  label = c("Individual Income Tax", "Sales Tax", "Corporate Income Tax")
)

top3 <- rev_long  %>% 
  filter(Category == "02" | Category == "03" | Category == "06") %>%
  ggplot()+
  geom_line(aes(x=Year, y=Dollars, color = Category_name)) + 
  geom_text(data = annotation, aes(x=x, y=y, label=label))+
    theme_bw() +
  
  scale_y_continuous(labels = comma)+
  scale_linetype_manual(values = c("dotted", "dashed", "solid")) +

  theme(legend.position = "none")+
  labs(title = "Top 3 Own Source Revenues", 
       subtitle = "Individual Income Taxes, Sales Tax, and Corporate income taxes",
       y = "Nominal Dollars (in Millions)") 
  

top3

12.0.2 Own Source and Fed Transfers

Code
ownsource_rev <- rev_long %>%
  filter(!Category %in% c("57", "58", "59")) %>%
  group_by(Year) %>% 
  summarize(Dollars = sum(Dollars))

# ownsource_rev %>% 
#   ggplot()+geom_line(aes(x=Year, y=Dollars)) + 
#   labs(title = "Own Source Revenues", subtitle = "Total own source revenue", y = "Millions of Dollars")

fed_rev <- ff_rev %>% select(fy, rev_57, rev_58, rev_59) %>%
  mutate(fed_total = rev_57+rev_58+rev_59)


annotation <- data.frame(
  x = c(2010, 2010),
  y = c(50000, 25000),  
  label = c("Own Source Revenue", "Federal Transfers")
)


ggplot() + 
  geom_line(data = ownsource_rev, aes(x=Year, y=Dollars), color = "Red") + 
  geom_line(data = fed_rev, aes(x=fy, y=fed_total), color = "Black") + 
    geom_text(data = annotation, aes(x=x, y=y, label=label))+
    scale_y_continuous(labels = comma)+
  theme(legend.position = "none")+

  theme_bw()+
  labs(title = "Own Source Revenue and Federal Transfers", 
  y = "Nominal Dollars (in Millions)")

13 CAGR / Growth

Each year, you will need to update the CAGR formulas! Change the filter() year.

calc_cagr is a function created for calculating the CAGRs for different spans of time.

Code
# function for calculating the CAGR
calc_cagr <- function(df, n) {
  df <- exp_long %>%
    select(-type) %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((`Dollars` / lag(`Dollars`, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(exp_long, 24) %>% 
  # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr23_precovid <- exp_long %>%
  filter(Year <= 2019) %>%
  calc_cagr(21) %>% 
  summarize(cagr_21 = round(sum(cagr*100, na.rm = TRUE), 2))



cagr_10 <- calc_cagr(exp_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(exp_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(exp_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(exp_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_1 <- calc_cagr(exp_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_expenditures_summary <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24 ) %>% 
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Expenditure Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

CAGR_expenditures_summary %>% # adorn_totals("row") %>% # totals calculated numbers, not what I want
  kbl(caption = "CAGR Calculations for Expenditure Categories") %>% 
  kable_styling(bootstrap_options = c("striped"))
CAGR Calculations for Expenditure Categories
Expenditure Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
Aging 6.35 6.87 7.18 -0.65 4.33 7.49
Agriculture 43.04 15.59 8.11 6.53 3.25 1.19
Bus & Profession Regulation 9.53 6.39 3.66 1.97 -1.55 1.49
Capital Improvement -6.53 17.27 18.12 10.65 -3.63 2.15
Central Management 2.05 1.06 8.53 1.18 4.71 4.46
Children And Family Services 3.98 4.60 5.53 4.71 1.30 0.17
Commerce And Economic Opportunity -24.04 52.43 35.74 17.29 3.44 4.83
Corrections 1.52 3.16 1.13 5.12 2.48 2.13
Debt Service -0.83 1.59 -0.70 1.65 1.19 6.11
Elected Officers 7.38 7.22 3.48 6.78 4.29 3.88
Employment Security -2.76 16.01 12.88 10.42 1.65 2.37
Environmental Protect Agency -1.98 -4.09 -7.73 -6.49 0.12 3.21
Family Services Net Medicaid 2.95 7.37 -6.65 0.81 -2.87 5.45
Human Services 15.30 12.23 10.33 6.90 3.66 2.75
Il State Toll Highway Auth 7.21 4.76 6.32 3.60 11.66 7.54
Judicial 4.20 6.41 9.15 5.11 3.40 2.99
K-12 Education 14.51 11.07 9.44 7.39 4.53 4.30
Legislative 24.13 13.97 12.12 8.15 2.76 3.35
Local Govt Transfers 44.48 26.75 16.73 9.93 6.42 4.66
Medicaid 10.11 13.93 15.00 10.14 8.99 7.25
Natural Resources 3.90 4.22 2.19 5.39 2.85 1.76
Other Boards & Commissions 2.96 10.05 3.68 3.20 -2.54 4.23
Other Departments 1.94 4.84 8.22 5.63 7.06 9.10
Public Health -0.16 29.65 29.12 20.32 8.71 7.63
Public Safety -9.74 10.35 21.41 17.00 8.62 6.11
Revenue 34.07 41.36 55.45 35.64 16.19 7.24
State Employee Healthcare 4.47 0.47 -1.52 -1.95 2.49 6.08
State Pension Contribution 15.42 10.80 9.67 9.26 9.38 10.76
Transportation -18.40 3.31 8.10 0.84 -0.24 3.35
University Education 4.72 2.44 3.92 -0.72 -0.76 0.44
Code
# to have it as a csv, uncomment the line below
#write_csv(CAGR_expenditures_summary, "CAGR_expenditures_summary.csv")
Code
calc_cagr <- function(df, n) {
  df <- rev_long %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((Dollars / lag(Dollars, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(rev_long, 24) %>% 
     # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr_10 <- calc_cagr(rev_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(rev_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(rev_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(rev_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

 cagr_1 <- calc_cagr(rev_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_revenue_summary <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24) %>%   
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Revenue Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

CAGR_revenue_summary %>% 
  kbl(caption = "CAGR Calculations for Revenue Sources") %>% 
  kable_styling(bootstrap_options = c("striped"))
CAGR Calculations for Revenue Sources
Revenue Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
All Other Sources 37.70 12.92 13.64 8.08 6.28 4.54
Cigarette Taxes -8.25 -0.54 3.02 1.49 3.33 2.51
Corp Franchise Taxes & Fees -32.40 1.21 -4.37 0.85 1.18 2.55
Corporate Income Taxes 76.66 72.77 38.19 32.31 13.59 7.70
Federal Medicaid 8.48 17.30 16.43 12.76 11.30 7.52
Federal Other 114.47 42.66 49.24 27.19 11.91 7.17
Federal Transportation -22.95 1.39 10.40 -2.73 -0.06 3.33
Gifts And Bequests 23.76 42.11 18.49 10.46 10.65 11.43
Individual Income Taxes 12.60 16.35 9.25 15.22 5.36 5.68
Inheritance Tax 35.98 48.20 16.36 18.47 10.12 3.74
Insurance Taxes&Fees&Licenses -3.42 12.76 5.20 2.79 3.20 6.56
Licenses, Fees & Registrations -4.68 15.06 16.83 9.26 6.23 7.87
Liquor Gallonage Taxes 2.53 2.81 2.49 1.69 1.37 7.45
Lottery Receipts -6.17 9.62 1.63 2.27 0.90 2.15
Medical Provider Assessments -1.98 3.67 16.26 11.80 8.33 8.36
Motor Fuel Tax 6.12 4.36 23.16 13.42 6.98 2.78
Motor Vehicle And Operators -5.59 4.66 -0.04 0.15 0.64 3.21
Other Taxes 63.89 32.74 17.36 13.92 17.13 7.87
Public Utility Taxes 3.09 -0.43 -1.43 0.22 -0.48 0.70
Receipts From Revenue Producing 3.01 4.78 -2.68 1.45 3.49 5.07
Riverboat Wagering Taxes 80.77 -1.03 -8.90 -6.18 -4.20 1.75
Sales Taxes 11.29 12.22 7.40 6.27 4.43 3.23
Code
# to have it as a csv, uncomment the line below
#write_csv(CAGR_revenue_summary, "CAGR_revenue_summary.csv")

rm(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24)

Expenditure and Revenue Growth using a lag formula:

Code
 exp_long %>% 
  group_by(Category_name) %>% 
  mutate(Growth = ((Dollars) - lag(Dollars))/lag(Dollars) *100) %>% 
  summarize(Growth = round(mean(Growth, na.rm = TRUE), 2))
Code
 rev_long %>% 
  group_by(Category_name) %>% 
  mutate(Growth = ((Dollars) - lag(Dollars))/lag(Dollars) *100) %>% 
  summarize(Growth = round(mean(Growth, na.rm = TRUE), 2))

14 Change from Previous Year

Code
revenue_change <- rev_long %>%
  select(-c(type,Category)) %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate(
    "FY 2022 Revenues ($ billions)" = round(Dollars_2022/1000, digits = 1),
#    "Change from 2021 to 2022" = round(Dollars_2022 - Dollars_2021, digits = 2),
         "Percent Change from 2021 to 2022" = round(((Dollars_2022 -Dollars_2021)/Dollars_2021*100), digits = 2)) %>%
  left_join(CAGR_revenue_summary, by = c("Category_name" = "Revenue Category")) %>% 
    arrange(-`FY 2022 Revenues ($ billions)`)%>%
  #select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  rename( "Compound Annual Growth, 1998-2022*" = `24 Year CAGR`, 
          "FY2022 Revenue Category" = Category_name ) %>%
  select(-c(Dollars_2021, Dollars_2022, `1 Year CAGR`:`10 Year CAGR`)) 

revenue_change %>% 
  kbl(caption = "Yearly Change in Revenue") %>% 
  kable_styling(bootstrap_options = c("striped"))
Yearly Change in Revenue
FY2022 Revenue Category FY 2022 Revenues ($ billions) Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Individual Income Taxes 23.8 12.60 5.68
Federal Other 19.8 114.47 7.17
Federal Medicaid 19.0 8.48 7.52
Sales Taxes 15.4 11.29 3.23
Corporate Income Taxes 9.7 76.66 7.70
Medical Provider Assessments 3.7 -1.98 8.36
All Other Sources 2.7 37.70 4.54
Motor Fuel Tax 2.5 6.12 2.78
Receipts From Revenue Producing 2.4 3.01 5.07
Licenses, Fees & Registrations 1.9 -4.68 7.87
Gifts And Bequests 1.9 23.76 11.43
Federal Transportation 1.8 -22.95 3.33
Motor Vehicle And Operators 1.6 -5.59 3.21
Public Utility Taxes 1.4 3.09 0.70
Lottery Receipts 1.4 -6.17 2.15
Other Taxes 1.4 63.89 7.87
Cigarette Taxes 0.8 -8.25 2.51
Inheritance Tax 0.6 35.98 3.74
Insurance Taxes&Fees&Licenses 0.6 -3.42 6.56
Liquor Gallonage Taxes 0.3 2.53 7.45
Riverboat Wagering Taxes 0.3 80.77 1.75
Corp Franchise Taxes & Fees 0.2 -32.40 2.55
Code
expenditure_change <- exp_long %>%
  select(-c(type,Category)) %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate("FY 2022 Expenditures ($ billions)" = round(Dollars_2022/1000, digits = 1),
  #  "Change from 2021 to 2022" = Dollars_2022 - Dollars_2021,
         "Percent Change from 2021 to 2022" = round((Dollars_2022 -Dollars_2021)/Dollars_2021*100, digits = 2) )%>%
  left_join(CAGR_expenditures_summary, by = c("Category_name" = "Expenditure Category")) %>% 
  arrange(-`FY 2022 Expenditures ($ billions)`)%>%
  select(-c(Dollars_2022, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  rename( "Compound Annual Growth, 1998-2022*" = `24 Year CAGR`, 
          "FY2022 Expenditure Category" = Category_name )

expenditure_change %>% 
  kbl(caption = "Yearly Change in Expenditures") %>% 
  kable_styling(bootstrap_options = c("striped"))
Yearly Change in Expenditures
FY2022 Expenditure Category FY 2022 Expenditures ($ billions) Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Medicaid 28.9 10.11 7.25
K-12 Education 13.9 14.51 4.30
Local Govt Transfers 10.4 44.48 4.66
Human Services 7.6 15.30 2.75
State Pension Contribution 6.5 15.42 10.76
Other Departments 4.9 1.94 9.10
Transportation 4.4 -18.40 3.35
State Employee Healthcare 3.0 4.47 6.08
Revenue 2.3 34.07 7.24
University Education 2.3 4.72 0.44
Il State Toll Highway Auth 2.1 7.21 7.54
Debt Service 2.0 -0.83 6.11
Public Safety 1.8 -9.74 6.11
Corrections 1.6 1.52 2.13
Children And Family Services 1.4 3.98 0.17
Commerce And Economic Opportunity 1.4 -24.04 4.83
Aging 1.2 6.35 7.49
Central Management 1.2 2.05 4.46
Elected Officers 1.0 7.38 3.88
Public Health 0.9 -0.16 7.63
Environmental Protect Agency 0.7 -1.98 3.21
Judicial 0.5 4.20 2.99
Family Services Net Medicaid 0.4 2.95 5.45
Capital Improvement 0.4 -6.53 2.15
Natural Resources 0.3 3.90 1.76
Employment Security 0.3 -2.76 2.37
Other Boards & Commissions 0.3 2.96 4.23
Bus & Profession Regulation 0.2 9.53 1.49
Agriculture 0.1 43.04 1.19
Legislative 0.1 24.13 3.35

Saves main items in one excel file named summary_file.xlsx. Delete eval=FALSE to run on local computer.

Code
#install.packages("openxlsx")
library(openxlsx)

dataset_names <- list('rev_long' = rev_long, 'exp_long' = exp_long, 
                      `Table 1` = expenditure_change, `Table 2` = revenue_change,
                      'Table 4.a' = CAGR_revenue_summary, 'Table 4.b' = CAGR_expenditures_summary, 
                      'year_totals' = year_totals)

write.xlsx(dataset_names, file = 'summary_file_FY2022.xlsx')

15 Drop COVID Dollars

If only sustainable revenues are included in the model, then the federal dollars from the pandemic response (CARES, CRSSA,& ARPA)should be excluded from the calculation of the fiscal gap.

The Fiscal Futures model focuses on sustainable revenue sources. To understand our fiscal gap and outlook, we need to exclude these one time revenues. GOMB has emphasized that they have allocated COVID dollars to one time expenditures (unemployment trust fund, budget stabilization fund, etc.). The fiscal gap, graphs,and CAGRs have been recalculated in the Drop COVID Dollars section below. In addition, an attempt at forecasting revenue and expenditures is also made after dropping the federal COVID dollars.

NOTE: I have only dropped State and Local CURE revenue so far. Federal money went into other funds during the beginning of pandemic.

  • fund 0628 is essential government support services. Money in the fund is appropriated to cover COVID-19 related expenses. It should be included in our analytical frame based on criteria 2 and6 — the fund supports an important state function about public safety, which would have to be performed even the fund structure were not existed. Public safety is supported by a combination of departments and boards, including IL Emergency Management Agency, which is the administering agency of the fund.

  • Education Stabilization Fund

  • ESSER

  • CSLFRF

  • Provider Relief Fund

  • Coronavirus Relief Fund (CRF)

  • Consolidated Appropriations Act

  • Families First Cornovirus Response Act

  • Paycheck Protection Program and Health Care Enhancement Act

Code
rev_temp <- rev_temp %>%  filter(covid_dollars==0) # keeps observations that were not coded as COVID federal funds


ff_rev <- rev_temp %>% 
  group_by(rev_type_new, fy) %>% 
  summarize(sum_receipts = sum(receipts, na.rm=TRUE)/1000000 ) %>%
  pivot_wider(names_from = "rev_type_new", values_from = "sum_receipts", names_prefix = "rev_")

ff_rev<- left_join(ff_rev, tax_refund)

ff_rev <- mutate_all(ff_rev, ~replace_na(.,0))


ff_rev <- ff_rev %>%
  mutate(rev_02 = rev_02 - ref_02,
         rev_03 = rev_03 - ref_03,
         rev_06 = rev_06 - ref_06,
         rev_09 = rev_09 - ref_09,
         rev_21 = rev_21 - ref_21,
         rev_24 = rev_24 - ref_24,
         rev_35 = rev_35 - ref_35

         ) %>% 
  select(-c(ref_02:ref_35, rev_99, rev_NA, rev_76
            ))

rev_long <- pivot_longer(ff_rev, rev_02:rev_78, names_to = c("type","Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy) %>%
  mutate(Category_name = case_when(
    Category == "02" ~ "INDIVIDUAL INCOME TAXES" ,
    Category == "03" ~ "CORPORATE INCOME TAXES" ,
    Category == "06" ~ "SALES TAXES" ,
    Category == "09" ~ "MOTOR FUEL TAX" ,
    Category == "12" ~ "PUBLIC UTILITY TAXES" ,
    Category == "15" ~ "CIGARETTE TAXES" ,
    Category == "18" ~ "LIQUOR GALLONAGE TAXES" ,
    Category == "21" ~ "INHERITANCE TAX" ,
    Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES" ,
    Category == "27" ~ "CORP FRANCHISE TAXES & FEES" ,
    Category == "30" ~ "HORSE RACING TAXES & FEES",  # in Other
    Category == "31" ~ "MEDICAL PROVIDER ASSESSMENTS" ,
    Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped
    Category == "33" ~  "LOTTERY RECEIPTS" ,
    Category == "35" ~  "OTHER TAXES" ,
    Category == "36" ~  "RECEIPTS FROM REVENUE PRODUCING", 
    Category == "39" ~  "LICENSES, FEES & REGISTRATIONS" ,
    Category == "42" ~  "MOTOR VEHICLE AND OPERATORS" ,
    Category == "45" ~  "STUDENT FEES-UNIVERSITIES",   # dropped
    Category == "48" ~  "RIVERBOAT WAGERING TAXES" ,
    Category == "51" ~  "RETIREMENT CONTRIBUTIONS" , # dropped
    Category == "54" ~ "GIFTS AND BEQUESTS", 
    Category == "57" ~  "FEDERAL OTHER" ,
    Category == "58" ~  "FEDERAL MEDICAID", 
    Category == "59" ~  "FEDERAL TRANSPORTATION" ,
    Category == "60" ~  "OTHER GRANTS AND CONTRACTS", #other
    Category == "63" ~  "INVESTMENT INCOME", # other
    Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped
    Category == "72" ~ "BOND ISSUE PROCEEDS",  #dropped
    Category == "75" ~  "INTER-AGENCY RECEIPTS ",  #dropped
    Category == "76" ~  "TRANSFER IN FROM OUT FUNDS",  #other
    Category == "78" ~  "ALL OTHER SOURCES" ,
    Category == "79" ~   "COOK COUNTY IGT", #dropped
    Category == "98" ~  "PRIOR YEAR REFUNDS", #dropped
                 T ~ "Check Me!"

  ) )%>% 
  mutate(Category_name = str_to_title(Category_name))






aggregated_totals_long <- rbind(rev_long, exp_long)

year_totals2 <- aggregated_totals_long %>% 
  group_by(type, Year) %>% 
  summarize(Dollars = sum(Dollars, na.rm = TRUE)) %>% 
  pivot_wider(names_from = "type", values_from = Dollars) %>% 
  rename(
         Expenditures = exp,
         Revenue = rev) %>%  
  mutate(`Fiscal Gap` = round(Revenue - Expenditures)) %>% 
  arrange(desc(Year))
# creates variable for the Gap each year

year_totals2
Code
annotation <- data.frame(
  x = c(2004, 2017, 2019),
  y = c(60000, 50000, 10000),  
  label = c("Expenditures","Revenue", "Fiscal Gap")
)

fiscal_gap_droppedCURE<-year_totals2 %>%  
  ggplot() +
  geom_hline(yintercept=0)+
  # geom_smooth adds regression line, graphed first so it appears behind line graph
  geom_smooth(aes(x = Year, y = Revenue), color = "gray", method = "lm", se = FALSE) + 
  geom_smooth(aes(x = Year, y = Expenditures), color = "rosybrown2", method = "lm", se = FALSE) +
  
  # line graph of revenue and expenditures
  geom_line(aes(x = Year, y = Revenue), color = "black", size=1) +
  geom_line(aes(x = Year, y = Expenditures), color = "red", size=1) +
  geom_line(aes(x=Year, y = `Fiscal Gap`), color="gray") +
  
  geom_text(data= annotation, aes(x=x, y = y, label=label))+
  
  # labels
    theme_bw() +
  scale_y_continuous(labels = comma)+
  xlab("Year") + 
  ylab("Millions of Dollars")  +
  ggtitle("Illinois Expenditures and Revenue Totals, 1998-2022")

fiscal_gap_droppedCURE

Compare with and without federal COVID dollars:

Code
library(gridExtra)

cowplot::plot_grid(fiscal_gap, fiscal_gap_droppedCURE)

Expenditure and revenue amounts in millions of dollars:

Code
exp_long %>%
  filter(Year == 2022) %>%
  #mutate(`Total Expenditures`= sum(Dollars, na.rm = TRUE)) %>%
 # select(-c(Year, `Total Expenditures`)) %>%
  arrange(desc(`Dollars`)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`, fill = "rosybrown2"))+ 
  coord_flip() +
      theme_bw() +
    theme(legend.position = "none") +

  labs(title = "Expenditures for FY2022") +
    xlab("Expenditure Categories") +
  ylab("Millions of Dollars") 

Code
rev_long %>%
  filter(Year == 2022) %>%
  #mutate(`Total Expenditures`= sum(Dollars, na.rm = TRUE)) %>%
 # select(-c(Year, `Total Expenditures`)) %>%
  arrange(desc(`Dollars`)) %>%
  ggplot() + 
  geom_col(aes(x = fct_reorder(Category_name, `Dollars`), y = `Dollars`))+ 
  coord_flip() +
    theme_bw() +
      labs(title = "Revenues for FY2022")+
    xlab("Revenue Categories") +
  ylab("Millions of Dollars")

15.1 Forecasting attempt

Code
#### Revenues

year_totals2 <- year_totals2 %>% 
  arrange(Year)

#ts_rev <- year_totals %>% select(Year, Revenue ) %>% arrange(Year)

tsrev <- ts(year_totals2$Revenue, start ="1998", frequency = 1) # yearly data

# start(tsrev) # 1998, January
# end(tsrev)  ## 2022 
# summary(tsrev)
# plot(tsrev)
# abline(reg=lm(tsrev~time(tsrev)))


#### ARIMAs
mymodel <- auto.arima(tsrev, seasonal = FALSE)
mymodel # ARIMA (0, 1, 0) with drift
Series: tsrev 
ARIMA(0,1,1) with drift 

Coefficients:
         ma1     drift
      0.8781  3012.396
s.e.  0.1925  1372.353

sigma^2 = 14506095:  log likelihood = -231.63
AIC=469.26   AICc=470.46   BIC=472.79
Code
myforecastrev <- forecast(mymodel, h = 20)
#plot(myforecastrev,  xlab ="", ylab ="Total Revenue", main ="Chicago Revenue")

tsexp <- ts(year_totals2$Expenditures, start = "1998", frequency = 1)
model_exp<- auto.arima(tsexp, seasonal = FALSE)
model_exp # ARIMA (0,1,1) with drift
Series: tsexp 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2958.3626
s.e.   871.9642

sigma^2 = 19041068:  log likelihood = -234.69
AIC=473.38   AICc=473.95   BIC=475.73
Code
forecast_exp <- forecast(model_exp, h = 20) 
#plot(forecast_exp, xlab ="",  ylab ="Total Expenditures", main ="Chicago Expenditures")

p <- forecast(model_exp,  h = 20) %>% 
  autoplot() +
    ylab("Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Expenditures") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

summary(forecast_exp)

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: tsexp 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2958.3626
s.e.   871.9642

sigma^2 = 19041068:  log likelihood = -234.69
AIC=473.38   AICc=473.95   BIC=475.73

Error measures:
                   ME     RMSE      MAE        MPE     MAPE      MASE
Training set 1.130403 4185.425 2682.578 -0.6894152 4.326307 0.6400791
                   ACF1
Training set -0.1249597

Forecasts:
     Point Forecast     Lo 80    Hi 80     Lo 95    Hi 95
2023       105177.5  99585.33 110769.7  96625.01 113730.0
2024       108135.9 100227.34 116044.4  96040.80 120231.0
2025       111094.2 101408.29 120780.2  96280.86 125907.6
2026       114052.6 102868.23 125237.0  96947.58 131157.6
2027       117011.0 104506.46 129515.5  97886.97 136135.0
2028       119969.3 106271.33 133667.3  99020.04 140918.6
2029       122927.7 108132.16 137723.2 100299.87 145555.5
2030       125886.1 110068.96 141703.2 101695.90 150076.2
2031       128844.4 112067.86 145621.0 103186.88 154502.0
2032       131802.8 114118.73 149486.8 104757.36 158848.2
2033       134761.1 116213.96 153308.3 106395.67 163126.6
2034       137719.5 118347.60 157091.4 108092.73 167346.3
2035       140677.9 120514.95 160840.8 109841.35 171514.4
2036       143636.2 122712.18 164560.3 111635.66 175636.8
2037       146594.6 124936.15 168253.0 113470.86 179718.3
2038       149553.0 127184.21 171921.7 115342.91 183763.0
2039       152511.3 129454.14 175568.5 117248.41 187774.2
2040       155469.7 131744.04 179195.3 119184.44 191754.9
2041       158428.0 134052.27 182803.8 121148.51 195707.6
2042       161386.4 136377.39 186395.4 123138.41 199634.4
Code
annotation <- data.frame(
  x = c(2027, 2032),
  y = c(130000, 100000),  label = c("$114 ± 19 Billion in 2027","$128 ± 25 Billion in 2032 ")
)

p + geom_label(data = annotation, aes(x=x, y=y, label=label), size = 3) + labs(title = "Forecasted Expenditures", caption = "Projected values at 95% confidence interval. 
Dark blue represents 80% liklihood of falling with that range, 
                                                                               light blue represents 95% liklihood of being in projected range.")

Code
#### revenue chart
model_rev <- auto.arima(tsrev, seasonal = FALSE)
forecast_rev <- forecast(model_rev, h = 20)

q <- forecast(forecast_rev,  h = 20) %>% 
  autoplot() +
    ylab("Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Revenue") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

summary(forecast_rev)

Forecast method: ARIMA(0,1,1) with drift

Model Information:
Series: tsrev 
ARIMA(0,1,1) with drift 

Coefficients:
         ma1     drift
      0.8781  3012.396
s.e.  0.1925  1372.353

sigma^2 = 14506095:  log likelihood = -231.63
AIC=469.26   AICc=470.46   BIC=472.79

Error measures:
                   ME     RMSE      MAE        MPE     MAPE      MASE      ACF1
Training set 38.18265 3572.865 2673.742 -0.5331993 4.610784 0.7280497 -0.168285

Forecasts:
     Point Forecast    Lo 80    Hi 80     Lo 95    Hi 95
2023       108159.9 103278.1 113041.8 100693.74 115626.1
2024       111172.3 100786.2 121558.4  95288.11 127056.5
2025       114184.7 100331.5 128037.9  92998.11 135371.3
2026       117197.1 100585.4 133808.8  91791.68 142602.5
2027       120209.5 101236.2 139182.9  91192.27 149226.7
2028       123221.9 102150.0 144293.9  90995.14 155448.7
2029       126234.3 103254.6 149214.0  91089.91 161378.7
2030       129246.7 104505.9 153987.4  91408.98 167084.4
2031       132259.1 105874.6 158643.6  91907.43 172610.8
2032       135271.5 107339.7 163203.2  92553.57 177989.4
2033       138283.9 108886.2 167681.5  93324.07 183243.7
2034       141296.3 110502.4 172090.1  94201.18 188391.4
2035       144308.7 112179.3 176438.1  95170.98 193446.4
2036       147321.1 113909.4 180732.7  96222.37 198419.8
2037       150333.5 115687.0 184979.9  97346.29 203320.7
2038       153345.9 117507.1 189184.6  98535.23 208156.5
2039       156358.3 119365.6 193350.9  99782.92 212933.6
2040       159370.7 121259.1 197482.3 101084.01 217657.3
2041       162383.1 123184.4 201581.7 102433.93 222332.2
2042       165395.5 125139.1 205651.8 103828.73 226962.2
Code
annotation <- data.frame(
  x = c(2027, 2032),
  y = c(200000, 300000),  
  label = c("$120 billion in 2027","$135 billion in 2032")
)

q+ geom_label(data = annotation, aes(x=x, y=y, label=label), size = 3) +
  labs(caption = "after dropping federal covid dollars")

Code
autoplot(tsexp) +
  #geom_line(tsexp)+
  #geom_line(aes(model_rev))+
  autolayer(forecast_rev, series = "Revenue") +
  autolayer(forecast_exp, series = "Expenditure)", alpha = 0.5) +
  geom_line(year_totals, mapping= aes(x = Year, y = Revenue))  + guides(colour = guide_legend("Forecast")) + 
  labs(title = "Forecasted Revenue and Expenditures", caption = "Revenue without State and Local CURE Dollars")

Revenue forecasting using precovid trends:

Code
# revenue using precovid trends
tsrev <- ts(year_totals$Revenue, start ="1998", end = "2020", frequency = 1) # yearly data

tsexp2019 <- ts(year_totals$Expenditures, start ="1998", end = "2020", frequency = 1) # yearly data

#### revenue chart
model_rev <- auto.arima(tsrev, seasonal = FALSE)
forecast_rev <- forecast(model_rev, h = 23)

c <- forecast(forecast_rev,  h = 22) %>% 
  autoplot() +
    ylab("Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Revenue") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

summary(forecast_rev)

Forecast method: ARIMA(0,1,0) with drift

Model Information:
Series: tsrev 
ARIMA(0,1,0) with drift 

Coefficients:
          drift
      2130.7731
s.e.   522.5883

sigma^2 = 6294284:  log likelihood = -202.91
AIC=409.82   AICc=410.45   BIC=412.01

Error measures:
                   ME     RMSE      MAE        MPE     MAPE      MASE
Training set 1.266691 2397.281 1730.586 -0.3250278 3.211109 0.7071574
                    ACF1
Training set -0.08547373

Forecasts:
     Point Forecast     Lo 80     Hi 80     Lo 95     Hi 95
2021       80272.46  77057.25  83487.67  75355.23  85189.70
2022       82403.24  77856.24  86950.23  75449.21  89357.26
2023       84534.01  78965.10  90102.92  76017.10  93050.92
2024       86664.78  80234.36  93095.20  76830.31  96499.26
2025       88795.56  81606.13  95984.98  77800.28  99790.84
2026       90926.33  83050.71  98801.95  78881.60 102971.05
2027       93057.10  84550.46 101563.75  80047.31 106066.89
2028       95187.88  86093.89 104281.86  81279.83 109095.93
2029       97318.65  87673.02 106964.28  82566.93 112070.36
2030       99449.42  89282.04 109616.81  83899.75 114999.09
2031      101580.20  90916.55 112243.84  85271.56 117888.83
2032      103710.97  92573.16 114848.78  86677.15 120744.78
2033      105841.74  94249.14 117434.34  88112.39 123571.10
2034      107972.51  95942.30 120002.73  89573.89 126371.14
2035      110103.29  97650.84 122555.74  91058.91 129147.67
2036      112234.06  99373.22 125094.90  92565.11 131903.01
2037      114364.83 101108.19 127621.48  94090.54 134639.13
2038      116495.61 102854.63 130136.58  95633.53 137357.68
2039      118626.38 104611.61 132641.15  97192.64 140060.13
2040      120757.15 106378.30 135136.01  98766.59 142747.71
2041      122887.93 108153.99 137621.87 100354.31 145421.54
2042      125018.70 109938.03 140099.37 101954.81 148082.59
2043      127149.47 111729.87 142569.07 103567.23 150731.72
Code
annotation <- data.frame(
  x = c(2020, 2032),
  y = c(130000, 100000),  
  label = c("$93 ± __ Billion in 2027","$104 ± __ Billion in 2032")
)

c+ geom_label(data = annotation, aes(x=x, y=y, label=label), size = 3) + labs(title= "Revenue Forecasted using Pre-Covid Data", subtitle = "Own Source and Federal Revenues Combined")

Code
autoplot(tsexp2019) +
  #geom_line(tsexp)+
  #geom_line(aes(model_rev))+
  autolayer(forecast_rev, series = "Revenue") +
  autolayer(forecast_exp, series = "Expenditure)", alpha = 0.5) +
  geom_line(year_totals, mapping= aes(x = Year, y = Revenue))  + guides(colour = guide_legend("Forecast")) + 
  labs(title = "Forecasted Revenue and Expenditures", caption = "Using Pre-Covid revenue data (ending in FY2020)")

15.1.1 Federal Revenue

Code
fed_rev <- ff_rev %>% select(fy, rev_57, rev_58, rev_59) %>%
  mutate(fed_total = rev_57+rev_58+rev_59)

fed_ts57 <- ts(fed_rev$rev_57, start ="1998", frequency = 1) # yearly data

model_fed <- auto.arima(fed_ts57, seasonal = FALSE)
forecast_fed <- forecast(model_fed, h = 23)

fed57 <- forecast(forecast_fed,  h = 20) %>% 
  autoplot() +
    ylab("Nominal Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Federal Other Revenue") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

fed57

Code
fed_ts58 <- ts(fed_rev$rev_58, start ="1998", frequency = 1) # yearly data

model_fed <- auto.arima(fed_ts58, seasonal = FALSE)
forecast_fed <- forecast(model_fed, h = 23)

fed58 <- forecast(forecast_fed,  h = 20) %>% 
  autoplot() +
    ylab("Nominal Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Federal Transfers for Transportation") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

fed58

Code
fed_ts59 <- ts(fed_rev$rev_59, start ="1998", frequency = 1) # yearly data

model_fed <- auto.arima(fed_ts59, seasonal = FALSE)
forecast_fed <- forecast(model_fed, h = 23)

fed59 <- forecast(forecast_fed,  h = 20) %>% 
  autoplot() +
    ylab("Nominal Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Federal Medicaid Reimbursements") +
  theme_classic() +
  scale_y_continuous(labels = dollar )

fed59

Code
fed_tstotal <- ts(fed_rev$fed_total, start ="1998", frequency = 1) # yearly data

model_fed <- auto.arima(fed_tstotal, seasonal = FALSE)
forecast_fed <- forecast(model_fed, h = 23)

fedtotal <- forecast(forecast_fed,  h = 20) %>% 
  autoplot() +
    ylab("Nominal Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Federal Revenue WITHOUT Federal COVID Dollars", subtitle = "Sum of Transportation, Medicaid, and Other Federal Tranfers") +
  theme_classic() +
  scale_y_continuous(labels = dollar ) 

fedtotal

Code
fed_tstotal <- ts(fed_rev$fed_total, start ="1998", end = "2020", frequency = 1) # yearly data

model_fed <- auto.arima(fed_tstotal, seasonal = FALSE)
forecast_fed <- forecast(model_fed, h = 23)

fedtotal2 <- forecast(forecast_fed,  h = 20) %>% 
  autoplot() +
    ylab("Nominal Dollars (Millions)") +
  xlab("Year") +
  ggtitle("Forecasted Federal Revenue -- pre-COVID trends", subtitle = "Sum of Transportation, Medicaid, and Other Federal Tranfers") +
  theme_classic() +
  scale_y_continuous(labels = dollar ) 

fedtotal2

Graphing the 3 federal revenue types together may be the most reliable since some COVID funding is still recorded in Federal Other and some are in other categories (like Disaster Response in FY2021). Need to look at more before using.

15.2 Tables with Totals

Code
exp_totals <- ff_exp %>% rowwise() %>% mutate(exp_TOTALS = sum(across(exp_402:exp_970)))
rev_totals <- ff_rev %>%    rowwise() %>% 
  mutate(rev_TOTALS = sum(across(rev_02:rev_78)))

rev_long <- pivot_longer(rev_totals, rev_02:rev_TOTALS, names_to = c("type","Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy) %>%
  mutate(Category_name = case_when(
    Category == "02" ~ "INDIVIDUAL INCOME TAXES" ,
    Category == "03" ~ "CORPORATE INCOME TAXES" ,
    Category == "06" ~ "SALES TAXES" ,
    Category == "09" ~ "MOTOR FUEL TAX" ,
    Category == "12" ~ "PUBLIC UTILITY TAXES" ,
    Category == "15" ~ "CIGARETTE TAXES" ,
    Category == "18" ~ "LIQUOR GALLONAGE TAXES" ,
    Category == "21" ~ "INHERITANCE TAX" ,
    Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES" ,
    Category == "27" ~ "CORP FRANCHISE TAXES & FEES" ,
    Category == "30" ~ "HORSE RACING TAXES & FEES",  # in Other
    Category == "31" ~ "MEDICAL PROVIDER ASSESSMENTS" ,
    Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped
    Category == "33" ~  "LOTTERY RECEIPTS" ,
    Category == "35" ~  "OTHER TAXES" ,
    Category == "36" ~  "RECEIPTS FROM REVENUE PRODUCING", 
    Category == "39" ~  "LICENSES, FEES & REGISTRATIONS" ,
    Category == "42" ~  "MOTOR VEHICLE AND OPERATORS" ,
    Category == "45" ~  "STUDENT FEES-UNIVERSITIES",   # dropped
    Category == "48" ~  "RIVERBOAT WAGERING TAXES" ,
    Category == "51" ~  "RETIREMENT CONTRIBUTIONS" , # dropped
    Category == "54" ~ "GIFTS AND BEQUESTS", 
    Category == "57" ~  "FEDERAL OTHER" ,
    Category == "58" ~  "FEDERAL MEDICAID", 
    Category == "59" ~  "FEDERAL TRANSPORTATION" ,
    Category == "60" ~  "OTHER GRANTS AND CONTRACTS", #other
    Category == "63" ~  "INVESTMENT INCOME", # other
    Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped
    Category == "72" ~ "BOND ISSUE PROCEEDS",  #dropped
    Category == "75" ~  "INTER-AGENCY RECEIPTS ",  #dropped
    Category == "76" ~  "TRANSFER IN FROM OUT FUNDS",  #other
    Category == "78" ~  "ALL OTHER SOURCES" ,
    Category == "79" ~   "COOK COUNTY IGT", #dropped
    Category == "98" ~  "PRIOR YEAR REFUNDS", #dropped
Category == "TOTALS" ~ "Total"

  ) ) %>% 
  select(-type, -Category) %>%  # drop extra columns type and Category number
  group_by(Year, Category_name) %>%
  summarise(Dollars= round(sum(Dollars),digits=2)) %>% 
  mutate(Category_name = str_to_title(Category_name))

# creates wide version of table where each revenue source is a column
revenue_wide2 <- rev_long %>% pivot_wider(names_from = Category_name, 
              values_from = Dollars) %>%
#  relocate("Other Revenue Sources **", .after = last_col()) %>%
  relocate("Total", .after =  last_col())
Code
exp_long <- pivot_longer(exp_totals, exp_402:exp_TOTALS , names_to = c("type", "Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy ) %>% 
  mutate(Category_name = 
           case_when(
            Category == "402" ~ "AGING" ,
            Category == "406" ~ "AGRICULTURE", 
             Category == "416" ~ "Central Management",
            Category == "418" ~ "CHILDREN AND FAMILY SERVICES", 
             Category == "420" ~ "Community Development",
            Category == "422" ~ "NATURAL RESOURCES" ,
             Category == "426" ~ "CORRECTIONS",
            Category == "427" ~ "EMPLOYMENT SECURITY" ,
             Category == "444" ~ "Human Services" ,
           Category == "478" ~ "HEALTHCARE & FAM SER NET OF MEDICAID", 
            Category == "482" ~ "PUBLIC HEALTH", 
            Category == "492" ~ "REVENUE", 
             Category == "494" ~ "Transportation" ,
             Category == "532" ~ "ENVIRONMENTAL PROTECT AGENCY" ,
             Category == "557" ~ "Tollway" ,
             Category == "684" ~ "IL COMMUNITY COLLEGE BOARD", 
             Category == "691" ~ "IL STUDENT ASSISTANCE COMM" ,
             Category == "900" ~ "NOT IN FRAME",
             Category == "901" ~ "State Pension Contribution",
             Category == "903" ~ "Debt Service",
             Category == "904" ~ "State Employee Healthcare",
             Category == "910" ~ "LEGISLATIVE"  ,
             Category == "920" ~ "JUDICIAL" ,
             Category == "930" ~ "ELECTED OFFICERS" , 
             Category == "940" ~ "OTHER HEALTH-RELATED", 
             Category == "941" ~ "Public Safety" ,
             Category == "942" ~ "ECON DEVT & INFRASTRUCTURE" ,
             Category == "943" ~ "CENTRAL SERVICES",
             Category == "944" ~ "BUS & PROFESSION REGULATION" ,
             Category == "945" ~ "Medicaid" ,
             Category == "946" ~ "Capital Improvement" , 
             Category == "948" ~ "OTHER DEPARTMENTS" ,
             Category == "949" ~ "OTHER BOARDS & COMMISSIONS" ,
             Category == "959" ~ "K-12 Education" ,
             Category == "960" ~ "UNIVERSITY EDUCATION",
             Category == "970" ~ "Local Govt Revenue Sharing",
          Category == "TOTALS" ~ "Total") #,T ~ "All Other Expenditures **")
           ) %>% 
  select(-type, -Category) %>% 
  group_by(Year, Category_name) %>% 
  summarise(Dollars= round(sum(Dollars),digits=2)) %>% 
  mutate(Category_name = str_to_title(Category_name))

expenditure_wide2 <- exp_long%>% 
  pivot_wider(names_from = Category_name, 
              values_from = Dollars) %>%
  #relocate("All Other Expenditures **", .after = last_col()) %>%
  relocate("Total", .after =  last_col())
Code
# function for calculating the CAGR
calc_cagr <- function(df, n) {
  df <- exp_long %>%
    #select(-type) %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((`Dollars` / lag(`Dollars`, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(exp_long, 24) %>% 
  # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr23_precovid <- exp_long %>%
  filter(Year <= 2019) %>%
  calc_cagr(21) %>% 
  summarize(cagr_21 = round(sum(cagr*100, na.rm = TRUE), 2))



cagr_10 <- calc_cagr(exp_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(exp_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(exp_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(exp_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_1 <- calc_cagr(exp_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_expenditures_summary_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24 ) %>% 
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Expenditure Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

move_to_last <- function(df, n) df[c(setdiff(seq_len(nrow(df)), n), n), ]

CAGR_expenditures_summary_tot <- move_to_last(CAGR_expenditures_summary_tot, 29) 

#CAGR_expenditures_summary_tot <-   select(CAGR_expenditures_summary_tot, -1) 

CAGR_expenditures_summary_tot%>%   
  kbl(caption = "CAGR Calculations for Expenditure Categories" , row.names=FALSE) %>% 
     kable_classic() %>%
    row_spec(31, bold = T, color = "black", background = "gray")
CAGR Calculations for Expenditure Categories
Expenditure Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
Aging 6.35 6.87 7.18 -0.65 4.33 7.49
Agriculture 43.05 15.59 8.10 6.53 3.25 1.19
Bus & Profession Regulation 9.53 6.39 3.66 1.97 -1.55 1.48
Capital Improvement -6.53 17.27 18.12 10.65 -3.63 2.15
Central Management 2.05 1.06 8.53 1.18 4.71 4.46
Children And Family Services 3.98 4.60 5.53 4.71 1.30 0.17
Community Development -24.04 52.43 35.74 17.29 3.44 4.83
Corrections 1.52 3.16 1.13 5.12 2.48 2.13
Debt Service -0.83 1.59 -0.70 1.65 1.19 6.11
Elected Officers 7.38 7.22 3.48 6.78 4.29 3.88
Employment Security -2.77 16.01 12.87 10.41 1.65 2.37
Environmental Protect Agency -1.98 -4.09 -7.73 -6.49 0.12 3.21
Healthcare & Fam Ser Net Of Medicaid 2.95 7.37 -6.65 0.81 -2.87 5.45
Human Services 15.30 12.23 10.33 6.90 3.66 2.75
Judicial 4.20 6.41 9.15 5.11 3.40 2.99
K-12 Education 14.51 11.07 9.44 7.39 4.53 4.30
Legislative 24.13 13.97 12.12 8.15 2.76 3.35
Local Govt Revenue Sharing 44.48 26.75 16.73 9.93 6.42 4.66
Medicaid 10.11 13.93 15.00 10.14 8.99 7.25
Natural Resources 3.90 4.22 2.19 5.39 2.85 1.76
Other Boards & Commissions 2.96 10.05 3.68 3.20 -2.54 4.23
Other Departments 1.94 4.84 8.22 5.63 7.06 9.10
Public Health -0.16 29.65 29.12 20.32 8.71 7.63
Public Safety -9.74 10.35 21.41 17.00 8.62 6.11
Revenue 34.07 41.36 55.45 35.64 16.19 7.24
State Employee Healthcare 4.47 0.47 -1.52 -1.95 2.49 6.08
State Pension Contribution 15.42 10.80 9.67 9.26 9.38 10.76
Tollway 7.21 4.76 6.32 3.60 11.66 7.54
Transportation -18.40 3.31 8.10 0.84 -0.24 3.35
University Education 4.72 2.44 3.92 -0.72 -0.76 0.44
Total 9.88 11.94 11.18 7.35 5.50 5.07
Code
calc_cagr <- function(df, n) {
  df <- rev_long %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((Dollars / lag(Dollars, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(rev_long, 24) %>% 
     # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr_10 <- calc_cagr(rev_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(rev_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(rev_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(rev_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

 cagr_1 <- calc_cagr(rev_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_revenue_summary_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24) %>%   
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Revenue Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

CAGR_revenue_summary_tot <- move_to_last(CAGR_revenue_summary_tot,1)
CAGR_revenue_summary_tot <- move_to_last(CAGR_revenue_summary_tot,22)

CAGR_revenue_summary_tot %>% 
  kbl(caption = "CAGR Calculations for Revenue Sources", row.names = FALSE) %>% 
     kable_classic() %>%
    row_spec(23, bold = T, color = "black", background = "gray")
CAGR Calculations for Revenue Sources
Revenue Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
Cigarette Taxes -8.25 -0.54 3.02 1.49 3.33 2.51
Corp Franchise Taxes & Fees -32.40 1.22 -4.37 0.85 1.18 2.55
Corporate Income Taxes 76.66 72.77 38.19 32.31 13.59 7.70
Federal Medicaid 8.48 17.30 16.43 12.76 11.30 7.52
Federal Other 21.32 32.72 22.40 12.92 6.39 4.55
Federal Transportation -22.95 1.39 10.40 1.51 1.37 3.33
Gifts And Bequests 23.76 42.12 18.49 10.46 10.65 11.43
Individual Income Taxes 12.60 16.35 9.25 15.22 5.36 5.68
Inheritance Tax 35.98 48.20 16.36 18.47 10.12 3.74
Insurance Taxes&Fees&Licenses -3.42 12.76 5.20 2.79 3.20 6.56
Licenses, Fees & Registrations -4.68 15.06 16.83 9.26 6.23 7.87
Liquor Gallonage Taxes 2.53 2.81 2.49 1.69 1.37 7.45
Lottery Receipts -6.17 9.62 1.63 2.27 0.90 2.15
Medical Provider Assessments -1.98 3.67 16.26 11.80 8.33 8.36
Motor Fuel Tax 6.12 4.36 23.16 13.42 6.98 2.78
Motor Vehicle And Operators -5.59 4.66 -0.04 0.15 0.64 3.21
Other Taxes 63.89 32.74 17.36 13.92 17.13 7.87
Public Utility Taxes 3.09 -0.43 -1.43 0.22 -0.48 0.70
Receipts From Revenue Producing 3.01 4.78 -2.68 1.45 3.49 5.07
Riverboat Wagering Taxes 80.77 -1.03 -8.90 -6.18 -4.20 1.75
Sales Taxes 11.29 12.22 7.40 6.27 4.43 3.23
All Other Sources 37.70 12.92 13.64 8.08 6.28 4.54
Total 14.15 18.36 13.15 11.40 6.55 5.16
Code
rm(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24)
Code
revenue_change2 <- rev_long %>%
  #select(-c(Category)) %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate(
    "FY 2022 Revenues ($ billions)" = round(Dollars_2022/1000, digits = 1),
#    "Change from 2021 to 2022" = round(Dollars_2022 - Dollars_2021, digits = 2),
         "Percent Change from 2021 to 2022" = round(((Dollars_2022 -Dollars_2021)/Dollars_2021*100), digits = 2)) %>%
  left_join(CAGR_revenue_summary_tot, by = c("Category_name" = "Revenue Category")) %>% 
    arrange(-`FY 2022 Revenues ($ billions)`)%>%
  #select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  rename( "Compound Annual Growth, 1998-2022*" = `24 Year CAGR`, 
          "FY2022 Revenue Category" = Category_name ) %>%
  select(-c(Dollars_2021, Dollars_2022, `1 Year CAGR`:`10 Year CAGR`)) 


revenue_change2 <- move_to_last(revenue_change2,8)
revenue_change2 <- move_to_last(revenue_change2,1)

revenue_change2 %>% 
  kbl(caption = "Yearly Change in Revenue", row.names = FALSE) %>% 
   kable_classic() %>%
    row_spec(23, bold = T, color = "black", background = "gray")
Yearly Change in Revenue
FY2022 Revenue Category FY 2022 Revenues ($ billions) Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Individual Income Taxes 23.8 12.60 5.68
Federal Medicaid 19.0 8.48 7.52
Sales Taxes 15.4 11.29 3.23
Federal Other 10.9 21.32 4.55
Corporate Income Taxes 9.7 76.66 7.70
Medical Provider Assessments 3.7 -1.98 8.36
Motor Fuel Tax 2.5 6.12 2.78
Receipts From Revenue Producing 2.4 3.01 5.07
Gifts And Bequests 1.9 23.76 11.43
Licenses, Fees & Registrations 1.9 -4.68 7.87
Federal Transportation 1.8 -22.95 3.33
Motor Vehicle And Operators 1.6 -5.59 3.21
Lottery Receipts 1.4 -6.17 2.15
Other Taxes 1.4 63.89 7.87
Public Utility Taxes 1.4 3.09 0.70
Cigarette Taxes 0.8 -8.25 2.51
Inheritance Tax 0.6 35.98 3.74
Insurance Taxes&Fees&Licenses 0.6 -3.42 6.56
Liquor Gallonage Taxes 0.3 2.53 7.45
Riverboat Wagering Taxes 0.3 80.77 1.75
Corp Franchise Taxes & Fees 0.2 -32.40 2.55
All Other Sources 2.7 37.70 4.54
Total 104.5 14.15 5.16
Code
expenditure_change2 <- exp_long %>%
  #select(-c(type,Category)) %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate("FY 2022 Expenditures ($ billions)" = round(Dollars_2022/1000, digits = 1),
  #  "Change from 2021 to 2022" = Dollars_2022 - Dollars_2021,
         "Percent Change from 2021 to 2022" = round((Dollars_2022 -Dollars_2021)/Dollars_2021*100, digits = 2) )%>%
  left_join(CAGR_expenditures_summary_tot, by = c("Category_name" = "Expenditure Category")) %>% 
  arrange(-`FY 2022 Expenditures ($ billions)`)%>%
  select(-c(Dollars_2022, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  rename( "Compound Annual Growth, 1998-2022*" = `24 Year CAGR`, 
          "FY2022 Expenditure Category" = Category_name )

expenditure_change2 <- move_to_last(expenditure_change2, 1)

expenditure_change2 %>% 
  kbl(caption = "Yearly Change in Expenditures", row.names = FALSE) %>% 
  kable_classic() %>%
    row_spec(31, bold = T, color = "black", background = "gray")
Yearly Change in Expenditures
FY2022 Expenditure Category FY 2022 Expenditures ($ billions) Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Medicaid 28.9 10.11 7.25
K-12 Education 13.9 14.51 4.30
Local Govt Revenue Sharing 10.4 44.48 4.66
Human Services 7.6 15.30 2.75
State Pension Contribution 6.5 15.42 10.76
Other Departments 4.9 1.94 9.10
Transportation 4.4 -18.40 3.35
State Employee Healthcare 3.0 4.47 6.08
Revenue 2.3 34.07 7.24
University Education 2.3 4.72 0.44
Tollway 2.1 7.21 7.54
Debt Service 2.0 -0.83 6.11
Public Safety 1.8 -9.74 6.11
Corrections 1.6 1.52 2.13
Children And Family Services 1.4 3.98 0.17
Community Development 1.4 -24.04 4.83
Aging 1.2 6.35 7.49
Central Management 1.2 2.05 4.46
Elected Officers 1.0 7.38 3.88
Public Health 0.9 -0.16 7.63
Environmental Protect Agency 0.7 -1.98 3.21
Judicial 0.5 4.20 2.99
Capital Improvement 0.4 -6.53 2.15
Healthcare & Fam Ser Net Of Medicaid 0.4 2.95 5.45
Employment Security 0.3 -2.77 2.37
Natural Resources 0.3 3.90 1.76
Other Boards & Commissions 0.3 2.96 4.23
Bus & Profession Regulation 0.2 9.53 1.48
Agriculture 0.1 43.05 1.19
Legislative 0.1 24.13 3.35
Total 102.2 9.88 5.07
Code
#install.packages("openxlsx")
library(openxlsx)

dataset_names <- list('rev_long' = rev_long, 'exp_long' = exp_long, 
                      `Table 1` = expenditure_change2, `Table 2` = revenue_change2,
                      'Table 4.a' = CAGR_revenue_summary_tot, 'Table 4.b' = CAGR_expenditures_summary_tot, 
                      'year_totals' = year_totals)

write.xlsx(dataset_names, file = 'summary_file_FY2022_withTotals.xlsx')

Export summary file with Totals

Code
dataset_names <- list('Aggregate Revenues' = revenue_wide2, 
                      'Aggregate Expenditures' = expenditure_wide2, 

                      
                      'Table 1' = expenditure_change2, #Top categories with yearly change, 23 yr cagr
                      'Table 2' = revenue_change2,
                      
                     # 'Table 4.a' = CAGR_revenue_summary_majorcats, # Categories Match Table 1 in paper
                     # 'Table 4.b' = CAGR_expenditures_summary_majorcats, 
                                            
                     # 'Table 1-AllCats' = expenditure_change_allcats,  # All Categories by Year
                    #  'Table 2-AllCats' = revenue_change_allcats,
                      
                      'Table 4.a-AllCats' = CAGR_revenue_summary_tot, 
                      'Table 4.b-AllCats' = CAGR_expenditures_summary_tot, 
                      
                      'year_totals' = year_totals,    # Total Revenue, Expenditure, and Fiscal gap per year
                      
                      'aggregated_totals_long' = aggregated_totals_long # all data in long format. Good for creating pivot tables in Excel
                      )

write.xlsx(dataset_names, file = 'summary_file_FY22_wTotals.xlsx')

16 Summary Tables - Largest Categories

The 10 largest revenue sources and 13 largest expenditure sources remain separate categories and all other smaller sources/expenditures are combined into “All Other _____”. These condensed tables are typically used in the Fiscal Futures articles. They were manually created in past years but this hopefully automates the process a bit until final formatting stages.

  • take ff_rev and ff_exp data frames, which were in wide format, pivot them longer and mutate the Category_name variable to nicer labels. Keep largest categories separate and aggregate the rest.
Code
exp_totals <- ff_exp %>% rowwise() %>% mutate(exp_TOTALS = sum(across(exp_402:exp_970))) # creates total column too

rev_totals <- ff_rev %>% rowwise() %>% 
  mutate(rev_TOTALS = sum(across(rev_02:rev_78)))

rev_long <- pivot_longer(rev_totals, rev_02:rev_TOTALS, names_to = c("type","Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy) %>%
  mutate(Category_name = case_when(
    Category == "02" ~ "Income Tax" ,
    Category == "03" ~ "Corporate Income Tax" ,
    Category == "06" ~ "Sales Tax" ,
    Category == "09" ~ "Motor Fuel Taxes" ,
 #   Category == "12" ~ "PUBLIC UTILITY TAXES, gross of PPRT" ,
  #  Category == "15" ~ "CIGARETTE TAXES" ,
 #   Category == "18" ~ "LIQUOR GALLONAGE TAXES" ,
 #  Category == "21" ~ "INHERITANCE TAX" ,
  #  Category == "24" ~ "INSURANCE TAXES&FEES&LICENSES, net of refunds " ,
   # Category == "27" ~ "CORP FRANCHISE TAXES & FEES" ,
 #   Category == "30" ~ "HORSE RACING TAXES & FEES",  # in Other
    Category == "31" ~ "Medical Provider Assessments" ,
  #  Category == "32" ~ "GARNISHMENT-LEVIES" , # dropped
  #  Category == "33" ~  "LOTTERY RECEIPTS" ,
   # Category == "35" ~  "OTHER TAXES" ,
    Category == "36" ~  "Receipts from Revenue Producing", 
    Category == "39" ~  "Licenses, Fees, Registration" ,
   # Category == "42" ~  "MOTOR VEHICLE AND OPERATORS" ,
#    Category == "45" ~  "STUDENT FEES-UNIVERSITIES",   # dropped
#    Category == "48" ~  "RIVERBOAT WAGERING TAXES" ,
  #  Category == "51" ~  "RETIREMENT CONTRIBUTIONS" , # dropped
   # Category == "54" ~ "GIFTS AND BEQUESTS", 
    Category == "57" ~  "Federal Other" ,
    Category == "58" ~  "Federal Medicaid Reimbursements", 
    Category == "59" ~  "Federal Transportation" ,
 #   Category == "60" ~  "OTHER GRANTS AND CONTRACTS", #other
#    Category == "63" ~  "INVESTMENT INCOME", # other
 #   Category == "66" ~ "PROCEEDS,INVESTMENT MATURITIES" , #dropped
 #   Category == "72" ~ "BOND ISSUE PROCEEDS",  #dropped
 #   Category == "75" ~  "INTER-AGENCY RECEIPTS ",  #dropped
 #   Category == "76" ~  "TRANSFER IN FROM OUT FUNDS",  #other
   # Category == "78new" ~  "ALL OTHER SOURCES" ,
   # Category == "79" ~   "COOK COUNTY IGT", #dropped
 #   Category == "98" ~  "PRIOR YEAR REFUNDS", #dropped
                
Category == "TOTALS" ~ "Total Revenue",
T ~ "All Other Sources **" # any other Category number that was not specifically referenced is cobined into Other Revenue Sources

  ) ) %>% 
  select(-type, -Category) %>%  # drop extra columns type and Category number
  group_by(Year, Category_name) %>%
  summarise(Dollars= round(sum(Dollars),digits=2)) 

# revenue_wide # not actually in wide format yet. 
# has 10 largest rev sources separate and combined all others to Other in long data format. 


# creates wide version of table where each revenue source is a column
revenue_wide2 <- rev_long %>% pivot_wider(names_from = Category_name, 
              values_from = Dollars) %>%
  relocate("All Other Sources **", .after = last_col()) %>%
  relocate("Total Revenue", .after =  last_col())


exp_long <- pivot_longer(exp_totals, exp_402:exp_TOTALS , names_to = c("type", "Category"), values_to = "Dollars", names_sep = "_") %>% 
  rename(Year = fy ) %>% 
  mutate(Category_name = 
           case_when(
            # Category == "402" ~ "AGING" ,
           #  Category == "406" ~ "AGRICULTURE", 
             Category == "416" ~ "Central Management",
            # Category == "418" ~ "CHILDREN AND FAMILY SERVICES", 
             Category == "420" ~ "Community Development",
           #  Category == "422" ~ "NATURAL RESOURCES" ,
            # Category == "426" ~ "CORRECTIONS",
           #  Category == "427" ~ "EMPLOYMENT SECURITY" ,
             Category == "444" ~ "Human Services" ,
           #  Category == "478" ~ "HEALTHCARE & FAM SER NET OF MEDICAID", 
           #  Category == "482" ~ "PUBLIC HEALTH", 
           #  Category == "492" ~ "REVENUE", 
             Category == "494" ~ "Transportation" ,
           #  Category == "532" ~ "ENVIRONMENTAL PROTECT AGENCY" ,
             Category == "557" ~ "Tollway" ,
           #  Category == "684" ~ "IL COMMUNITY COLLEGE BOARD", 
            # Category == "691" ~ "IL STUDENT ASSISTANCE COMM" ,
           #  Category == "900" ~ "NOT IN FRAME",
             Category == "901" ~ "State Pension Contribution",
             Category == "903" ~ "Debt Service",
             Category == "904" ~ "State Employee Healthcare",
           #  Category == "910" ~ "LEGISLATIVE"  ,
          #   Category == "920" ~ "JUDICIAL" ,
          #   Category == "930" ~ "ELECTED OFFICERS" , 
            # Category == "940" ~ "OTHER HEALTH-RELATED", 
             Category == "941" ~ "Public Safety" ,
           #  Category == "942" ~ "ECON DEVT & INFRASTRUCTURE" ,
           #  Category == "943" ~ "CENTRAL SERVICES",
           #  Category == "944" ~ "BUS & PROFESSION REGULATION" ,
             Category == "945" ~ "Medicaid" ,
             Category == "946" ~ "Capital Improvement" , 
           #  Category == "948" ~ "OTHER DEPARTMENTS" ,
            # Category == "949" ~ "OTHER BOARDS & COMMISSIONS" ,
             Category == "959" ~ "K-12 Education" ,
           #  Category == "960" ~ "UNIVERSITY EDUCATION",
             Category == "970" ~ "Local Govt Revenue Sharing",
          Category == "TOTALS" ~ "Total Expenditures",
             T ~ "All Other Expenditures **")
           ) %>% 
  select(-type, -Category) %>% 
  group_by(Year, Category_name) %>% 
  summarise(Dollars= round(sum(Dollars),digits=2))

expenditure_wide2 <- exp_long%>% 
  pivot_wider(names_from = Category_name, 
              values_from = Dollars) %>%
  relocate("All Other Expenditures **", .after = last_col()) %>%
  relocate("Total Expenditures", .after =  last_col())


# CAGR values for largest expenditure categories and combined All Other Expenditures

# function for calculating the CAGR
calc_cagr <- function(df, n) {
  df <- exp_long %>%
    #select(-type) %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((`Dollars` / lag(`Dollars`, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(exp_long, 24) %>% 
  # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr23_precovid <- exp_long %>%
  filter(Year <= 2019) %>%
  calc_cagr(21) %>% 
  summarize(cagr_21 = round(sum(cagr*100, na.rm = TRUE), 2))



cagr_10 <- calc_cagr(exp_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(exp_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(exp_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(exp_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_1 <- calc_cagr(exp_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_expenditures_majorcats_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24 ) %>% 
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Expenditure Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

move_to_last <- function(df, n) df[c(setdiff(seq_len(nrow(df)), n), n), ]

CAGR_expenditures_majorcats_tot <- move_to_last(CAGR_expenditures_majorcats_tot, 1)
CAGR_expenditures_majorcats_tot <- move_to_last(CAGR_expenditures_majorcats_tot, 13) 


CAGR_expenditures_majorcats_tot%>%   
  kbl(caption = "CAGR Calculations for Largest Expenditure Categories" , row.names=FALSE) %>% 
     kable_classic() 
CAGR Calculations for Largest Expenditure Categories
Expenditure Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
Capital Improvement -6.53 17.27 18.12 10.65 -3.63 2.15
Central Management 2.05 1.06 8.53 1.18 4.71 4.46
Community Development -24.04 52.43 35.74 17.29 3.44 4.83
Debt Service -0.83 1.59 -0.70 1.65 1.19 6.11
Human Services 15.30 12.23 10.33 6.90 3.66 2.75
K-12 Education 14.51 11.07 9.44 7.39 4.53 4.30
Local Govt Revenue Sharing 44.48 26.75 16.73 9.93 6.42 4.66
Medicaid 10.11 13.93 15.00 10.14 8.99 7.25
Public Safety -9.74 10.35 21.41 17.00 8.62 6.11
State Employee Healthcare 4.47 0.47 -1.52 -1.95 2.49 6.08
State Pension Contribution 15.42 10.80 9.67 9.26 9.38 10.76
Tollway 7.21 4.76 6.32 3.60 11.66 7.54
Transportation -18.40 3.31 8.10 0.84 -0.24 3.35
All Other Expenditures ** 6.25 8.68 8.68 5.70 3.90 3.77
Total Expenditures 9.88 11.94 11.18 7.35 5.50 5.07
Code
# Yearly change for Top 13 largest expenditure categories
expenditure_change2 <- exp_long %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate("FY 2022 Expenditures" = round(Dollars_2022/1000, digits = 1),
         "FY 2021 Expenditures" = round(Dollars_2021/1000, digits = 1),
         "Percent Change from 2021 to 2022" = percent((Dollars_2022 -Dollars_2021)/Dollars_2021, accuracy = .1) )  %>%
  left_join(CAGR_expenditures_majorcats_tot, by = c("Category_name" = "Expenditure Category")) %>% 
  arrange(-`FY 2022 Expenditures`)%>%
  mutate(`24 Year CAGR` = percent(`24 Year CAGR`/100, accuracy=.1)) %>%
  select(-c(Dollars_2022, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  rename( "Compound Annual Growth, 1998-2022*" = `24 Year CAGR`, 
          "FY2022 Expenditure Category" = Category_name )

expenditure_change2 <- move_to_last(expenditure_change2, 3) 

expenditure_change2 <- move_to_last(expenditure_change2, 1)

expenditure_change2 %>% 
  kbl(caption = "Yearly Change in Expenditures", row.names = FALSE, align = "l") %>% 
  kable_classic() %>%
    row_spec(15, bold = T, color = "black", background = "gray")
Yearly Change in Expenditures
FY2022 Expenditure Category FY 2022 Expenditures FY 2021 Expenditures Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Medicaid 28.9 26.3 10.1% 7.2%
K-12 Education 13.9 12.2 14.5% 4.3%
Local Govt Revenue Sharing 10.4 7.2 44.5% 4.7%
Human Services 7.6 6.6 15.3% 2.8%
State Pension Contribution 6.5 5.6 15.4% 10.8%
Transportation 4.4 5.3 -18.4% 3.4%
State Employee Healthcare 3.0 2.9 4.5% 6.1%
Tollway 2.1 2.0 7.2% 7.5%
Debt Service 2.0 2.0 -0.8% 6.1%
Public Safety 1.8 2.0 -9.7% 6.1%
Community Development 1.4 1.9 -24.0% 4.8%
Central Management 1.2 1.2 2.0% 4.5%
Capital Improvement 0.4 0.5 -6.5% 2.1%
All Other Expenditures ** 18.5 17.5 6.3% 3.8%
Total Expenditures 102.2 93.0 9.9% 5.1%

Top 10 revenue sources CAGRs and Yearly Change Tables:

Code
##### Top 10 revenue CAGRs: ####


calc_cagr <- function(df, n) {
  df <- rev_long %>%
    arrange(Category_name, Year) %>%
    group_by(Category_name) %>%
    mutate(cagr = ((Dollars / lag(Dollars, n)) ^ (1 / n)) - 1)

  return(df)
}

# This works for one variable at a time
cagr_24 <- calc_cagr(rev_long, 24) %>% 
     # group_by(Category) %>%
  summarize(cagr_24 = round(sum(cagr*100, na.rm = TRUE), 2))

cagr_10 <- calc_cagr(rev_long, 10) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_10 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_5 <- calc_cagr(rev_long, 5) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_5 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_3 <- calc_cagr(rev_long, 3) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_3 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

cagr_2 <- calc_cagr(rev_long, 2) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_2 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

 cagr_1 <- calc_cagr(rev_long, 1) %>% 
  filter(Year == 2022) %>%
  summarize(cagr_1 = case_when(Year == 2022 ~ round(sum(cagr*100, na.rm = TRUE), 2)))

CAGR_revenue_majorcats_tot <- data.frame(cagr_1, cagr_2, cagr_3, cagr_5, cagr_10, cagr_24) %>%   
  select(-c(Category_name.1, Category_name.2, Category_name.3, Category_name.4, Category_name.5 )) %>% 
  rename("Revenue Category" = Category_name, "1 Year CAGR" = cagr_1, "2 Year CAGR" = cagr_2, "3 Year CAGR" = cagr_3, "5 Year CAGR" = cagr_5, "10 Year CAGR" = cagr_10,"24 Year CAGR" = cagr_24 )

CAGR_revenue_majorcats_tot <- move_to_last(CAGR_revenue_majorcats_tot,1)
CAGR_revenue_majorcats_tot <- move_to_last(CAGR_revenue_majorcats_tot,11)

CAGR_revenue_majorcats_tot %>% 
  kbl(caption = "CAGR Calculations for Revenue Sources", row.names = FALSE) %>% 
     kable_classic() 
CAGR Calculations for Revenue Sources
Revenue Category 1 Year CAGR 2 Year CAGR 3 Year CAGR 5 Year CAGR 10 Year CAGR 24 Year CAGR
Corporate Income Tax 76.66 72.77 38.19 32.31 13.59 7.70
Federal Medicaid Reimbursements 8.48 17.30 16.43 12.76 11.30 7.52
Federal Other 21.32 32.72 22.40 12.92 6.39 4.55
Federal Transportation -22.95 1.39 10.40 1.51 1.37 3.33
Income Tax 12.60 16.35 9.25 15.22 5.36 5.68
Licenses, Fees, Registration -4.68 15.06 16.83 9.26 6.23 7.87
Medical Provider Assessments -1.98 3.67 16.26 11.80 8.33 8.36
Motor Fuel Taxes 6.12 4.36 23.16 13.42 6.98 2.78
Receipts from Revenue Producing 3.01 4.78 -2.68 1.45 3.49 5.07
Sales Tax 11.29 12.22 7.40 6.27 4.43 3.23
All Other Sources ** 13.85 13.43 6.90 4.95 4.07 3.90
Total Revenue 14.15 18.36 13.15 11.40 6.55 5.16
Code
###### Yearly change summary table for Top 10 Revenues #####
revenue_change2 <- rev_long %>%
  #select(-c(Category)) %>%
  filter(Year > 2020) %>%
  pivot_wider(names_from = Year , values_from = Dollars,   names_prefix = "Dollars_") %>%
  mutate(
    "FY 2022 Revenues ($ billions)" = round(Dollars_2022/1000, digits = 1),
            "FY 2021 Revenues ($ billions)" = round(Dollars_2021/1000, digits = 1),

         "Percent Change from 2021 to 2022" = percent(((Dollars_2022 -Dollars_2021)/Dollars_2021), accuracy = .1)) %>%
  left_join(CAGR_revenue_majorcats_tot, by = c("Category_name" = "Revenue Category")) %>% 
    arrange(-`FY 2022 Revenues ($ billions)`)%>%
  #select(-c(Dollars_2021, Dollars_2021, `1 Year CAGR`:`10 Year CAGR`)) %>%
  mutate("Compound Annual Growth, 1998-2022*" = percent(`24 Year CAGR`/100, accuracy=.1)) %>%
  rename("FY2022 Revenue Category" = Category_name ) %>%
  select(-c(Dollars_2022, Dollars_2021, `1 Year CAGR`:`24 Year CAGR`)) 

revenue_change2 <- move_to_last(revenue_change2,5)

revenue_change2 <- move_to_last(revenue_change2,1)

revenue_change2%>% 
  kbl(caption = "Yearly Change in Revenue", row.names = FALSE, align = "l") %>% 
   kable_classic() %>%
    row_spec(12, bold = T, color = "black", background = "gray")
Yearly Change in Revenue
FY2022 Revenue Category FY 2022 Revenues ($ billions) FY 2021 Revenues ($ billions) Percent Change from 2021 to 2022 Compound Annual Growth, 1998-2022*
Income Tax 23.8 21.2 12.6% 5.7%
Federal Medicaid Reimbursements 19.0 17.6 8.5% 7.5%
Sales Tax 15.4 13.9 11.3% 3.2%
Federal Other 10.9 9.0 21.3% 4.6%
Corporate Income Tax 9.7 5.5 76.7% 7.7%
Medical Provider Assessments 3.7 3.8 -2.0% 8.4%
Motor Fuel Taxes 2.5 2.4 6.1% 2.8%
Receipts from Revenue Producing 2.4 2.3 3.0% 5.1%
Licenses, Fees, Registration 1.9 2.0 -4.7% 7.9%
Federal Transportation 1.8 2.4 -22.9% 3.3%
All Other Sources ** 13.3 11.7 13.9% 3.9%
Total Revenue 104.5 91.6 14.2% 5.2%

16.0.1 Export Summary Files

Saves main items in one excel file named summary_file.xlsx. Delete eval=FALSE to run on local computer.

Code
#install.packages("openxlsx")
library(openxlsx)

dataset_names <- list('Aggregate Revenues' = revenue_wide2, # Top Categories aggregated, nice labels
                      'Aggregate Expenditures' = expenditure_wide2, 

                      
                      'Table 1' = expenditure_change2, #Top categories with yearly change, 23 yr cagr
                      'Table 2' = revenue_change2,
                      
                      'Table 4.a' = CAGR_revenue_summary_majorcats, # Categories Match Table 1 in paper
                      'Table 4.b' = CAGR_expenditures_summary_majorcats, 
                                            
                      'Table 1-AllCats' = expenditure_change_allcats,  # All Categories by Year
                      'Table 2-AllCats' = revenue_change_allcats,
                      
                      'Table 4.a-AllCats' = CAGR_revenue_summary_allcats, 
                      'Table 4.b-AllCats' = CAGR_expenditures_summary_allcats, 
                      
                      'year_totals' = year_totals,    # Total Revenue, Expenditure, and Fiscal gap per year
                      
                      'aggregated_totals_long' = aggregated_totals_long # all data in long format. Good for creating pivot tables in Excel
                      )

write.xlsx(dataset_names, file = 'summary_file_FY22_MajorCats_WithTotals.xlsx')